CN111465822A

CN111465822A - Ground point cloud map precision evaluation method, device and system and unmanned aerial vehicle

Info

Publication number: CN111465822A
Application number: CN201880072752.5A
Authority: CN
Inventors: 薛唐立
Original assignee: SZ DJI Technology Co Ltd
Current assignee: SZ DJI Technology Co Ltd
Priority date: 2018-11-26
Filing date: 2018-11-26
Publication date: 2020-07-28
Also published as: WO2020107174A1

Abstract

Provided are a method, a device and a system for evaluating ground point cloud map precision and an unmanned aerial vehicle. The method comprises the following steps: determining current position information and absolute height information of an unmanned aerial vehicle through positioning equipment carried by the unmanned aerial vehicle, and determining current relative height information (101) of the unmanned aerial vehicle relative to the ground through an airborne radar of the unmanned aerial vehicle; acquiring altitude data of a flight path of the unmanned aerial vehicle according to the position information, the absolute altitude information and the relative altitude information; the elevation data of the flight path is used to assess the accuracy of a ground point cloud map (102). The method simplifies the operation of evaluating the precision of the ground point cloud map and improves the reliability of the precision.

Description

Ground point cloud map precision evaluation method, device and system and unmanned aerial vehicle

Technical Field

The invention relates to the technical field of measurement, in particular to a method, a device and a system for evaluating ground point cloud map precision and an unmanned aerial vehicle.

Background

Currently, the spatial distribution of regional geographic modalities can be described by a ground point cloud map. The ground point cloud map may be, for example, a Digital Surface Model (DSM) map.

In the prior art, a ground point cloud map includes: longitude location, latitude location, and elevation value. And, the accuracy of the ground point cloud map is usually determined by comparing the elevation of the image control point with the elevation of the image control point in the ground point cloud map. It can be seen that the evaluation of the accuracy of the ground point cloud map depends heavily on the deployment and control of the image control points.

Therefore, in the prior art, when the accuracy of the ground point cloud map is evaluated, the operation is complex, and the reliability of the accuracy is low.

Disclosure of Invention

The embodiment of the invention provides a method, a device and a system for evaluating the precision of a ground point cloud map and an unmanned aerial vehicle, which are used for solving the problems of complex operation and low reliability of precision when the precision of the ground point cloud map is evaluated in the prior art.

In a first aspect, an embodiment of the present invention provides a method for evaluating accuracy of a ground point cloud map, including:

determining current position information and absolute height information of the unmanned aerial vehicle through positioning equipment carried by the unmanned aerial vehicle, and determining current relative height information of the unmanned aerial vehicle relative to the ground through an airborne radar of the unmanned aerial vehicle;

acquiring altitude data of a flight path of the unmanned aerial vehicle according to the position information, the absolute altitude information and the relative altitude information; and the elevation data of the flight route is used for evaluating the precision of the ground point cloud map.

In a second aspect, an embodiment of the present invention provides a system for evaluating accuracy of a ground point cloud map, including: a processor and a memory;

the memory for storing program code;

the processor, invoking the program code, when executed, is configured to:

In a third aspect, an embodiment of the present invention provides a device for evaluating accuracy of a ground point cloud map, including: positioning equipment, an airborne radar and the ground point cloud map precision evaluation system of any one of the second aspect.

In a fourth aspect, an embodiment of the present invention provides an unmanned aerial vehicle, including: positioning equipment, an airborne radar and a processor;

the processor is used for determining the current position information and the absolute height information of the unmanned aerial vehicle through the positioning equipment;

the processor is further used for determining the current relative height information of the unmanned aerial vehicle relative to the ground through the airborne radar;

the processor is further configured to obtain elevation data of a flight path of the unmanned aerial vehicle according to the position information, the absolute altitude information and the relative altitude information; and the altitude data of the flight path of the unmanned aerial vehicle is used for evaluating the precision of the ground point cloud map.

In a fifth aspect, the present invention provides a computer-readable storage medium storing a computer program, where the computer program includes at least one code segment executable by a computer to control the computer to execute the point cloud map accuracy assessment method according to any one of the above first aspects.

In a sixth aspect, an embodiment of the present invention provides a computer program, which is configured to implement the point cloud map accuracy assessment method according to any one of the above first aspects when the computer program is executed by a computer.

According to the ground point cloud map precision evaluation method, the device and the system provided by the embodiment of the invention, the current position information and the absolute height information of the unmanned aerial vehicle are determined through the positioning equipment carried by the unmanned aerial vehicle, the current relative height information of the unmanned aerial vehicle relative to the ground is determined through the airborne radar of the unmanned aerial vehicle, and the altitude data of the flight path of the unmanned aerial vehicle is obtained according to the position information, the absolute height information and the relative height information, wherein the altitude data of the flight path is used for evaluating the precision of the ground point cloud map.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

Fig. 1 is a schematic flow chart of a ground point cloud map accuracy evaluation method according to an embodiment of the present invention;

FIG. 2 is a schematic illustration of elevation data for a waypoint provided in accordance with an embodiment of the invention;

fig. 3 is a schematic flow chart of a ground point cloud map accuracy evaluation method according to another embodiment of the present invention;

fig. 4 is a schematic flow chart of a ground point cloud map accuracy evaluation method according to another embodiment of the present invention;

FIG. 5 is a schematic diagram of a continuous wave radar ranging system according to an embodiment of the present invention;

FIG. 6 is a diagram illustrating ranging of a continuous wave radar within a predicted angle range according to an embodiment of the present invention;

fig. 7 is a schematic flow chart of a ground point cloud map accuracy evaluation method according to another embodiment of the present invention;

fig. 8A to 8C are schematic diagrams illustrating obtaining M first default elements according to an embodiment of the present invention;

FIG. 9A is a diagram illustrating a fitted straight line obtained from first ranging data without culling data having a clustering density less than a predetermined density according to the prior art;

fig. 9B is a schematic diagram of a fitted straight line obtained according to the second distance measurement data after the data with the clustering density smaller than the preset density is removed according to an embodiment of the present invention;

fig. 10 is a flowchart illustrating a method for evaluating accuracy of a ground point cloud map according to another embodiment of the present invention;

fig. 11 is a schematic structural diagram of a ground point cloud map accuracy evaluation system according to an embodiment of the present invention;

fig. 12 is a schematic structural diagram of a ground point cloud map precision evaluation apparatus according to an embodiment of the present invention;

fig. 13 is a schematic structural diagram of the unmanned aerial vehicle according to the embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The ground point cloud map precision evaluation method provided by the embodiment of the invention can determine the current position information, the absolute height information and the relative height information relative to the ground of the unmanned aerial vehicle through the positioning equipment and the airborne radar carried by the unmanned aerial vehicle, and obtain the altitude data of the flight path of the unmanned aerial vehicle through the current position information, the absolute height information and the relative height information, wherein the altitude data of the flight path is used for evaluating the precision of the ground point cloud map.

Alternatively, the drone may be an aircraft, such as a rotorcraft (rotorcraft), for example, a multi-rotor aircraft propelled through the air by a plurality of propulsion devices, but other types of aircraft are possible, as embodiments of the invention are not limited in this respect.

Alternatively, the positioning device may include a Real-time kinematic (RTK) mobile terminal. The RTK positioning can be realized based on an RTK mobile terminal, and the RTK positioning is based on a real-time dynamic positioning technology of a carrier phase observation value, can provide a three-dimensional positioning result of an object to be positioned (such as an unmanned aerial vehicle) in a specified coordinate system in real time and reaches centimeter-level precision.

In the RTK positioning process, the RTK base station transmits observation data of a Global Navigation Satellite System (GSSN) acquired by the RTK base station and coordinate information of the GSSN to the RTK mobile terminal. The RTK mobile terminal not only receives data from the RTK base station, but also acquires observation data of the GSSN, forms a differential observation value in the system for real-time processing, and simultaneously gives a centimeter-level positioning result for less than one second. The RTK mobile terminal can be in a static state or a moving state.

It should be noted that the positioning device RTK that sets up on unmanned aerial vehicle removes the end can be one or more, when the RTK removes the end and is a plurality of, can improve positioning accuracy.

Wherein, the airborne radar is used for measuring the relative altitude information of unmanned aerial vehicle relative to ground. Alternatively, the airborne radar may be a continuous wave radar or a lidar. Because the laser radar has the characteristics of high resolution and the like, high-precision relative height information can be obtained through the laser radar. High accuracy of relative altitude information may also be obtained by continuous wave radar, as described below in relation to the embodiment of fig. 4. Alternatively, the continuous wave radar may be specifically a millimeter wave radar.

Optionally, the ground point cloud map may include: a DSM Map, a Digital Elevation Model (DEM) Map, or a Digital Orthophoto Map (DOM).

Fig. 1 is a schematic flow chart of a ground point cloud map accuracy evaluation method according to an embodiment of the present invention, where an execution main body of the embodiment may be an unmanned aerial vehicle, and specifically may be a processor of the unmanned aerial vehicle. As shown in fig. 1, the method of this embodiment may include:

step 101, determining current position information and absolute height information of the unmanned aerial vehicle through positioning equipment carried by the unmanned aerial vehicle, and determining current relative height information of the unmanned aerial vehicle relative to the ground through an airborne radar of the unmanned aerial vehicle.

In this step, optionally, the positioning device may specifically be an RTK mobile terminal. Alternatively, the location information may be absolute location information, such as latitude and longitude information; alternatively, the position information may be relative position information, such as position information relative to an RTK base station. The absolute height information may also be referred to as altitude information, among others.

Optionally, the airborne radar of the drone may emit radar waves in the direction of gravity to obtain relative altitude information of the drone with respect to the ground. Specifically, when the unmanned aerial vehicle flies flatly, the airborne radar can emit radar waves to the position right below the unmanned aerial vehicle. It should be noted that, since the radar wave emitted by the airborne radar is emitted to the ground, and may be reflected by the ground or emitted by an object (e.g., a tree, a building, etc.) on the ground, the relative height information of the drone with respect to the ground may include: relative height information with respect to the ground surface, and/or relative height information with respect to objects on the ground surface.

The current position information of the unmanned aerial vehicle can be used for determining the flight line of the unmanned aerial vehicle. The current absolute altitude information of the unmanned aerial vehicle and the current relative altitude information of the unmanned aerial vehicle relative to the ground can be used for determining the altitude data of the flight path.

Alternatively, the absolute height information may specifically be an absolute height value, and the relative height information may specifically be a relative height value.

Optionally, the drone comprises a positioning device and an airborne radar.

Optionally, step 101 may be triggered to be executed by a time condition, for example, step 101 may be triggered to be executed every 1 second; alternatively, step 101 may be triggered by distance, for example every 1 meter to perform step 101; alternatively, both time conditions and distance conditions may trigger the execution of step 101, for example every 1 second and every 1 meter.

102, obtaining elevation data of a flight path of the unmanned aerial vehicle according to the position information, the absolute height information and the relative height information; and the elevation data of the flight route is used for evaluating the precision of the ground point cloud map.

In this step, the flight path of the unmanned aerial vehicle may be composed of one or more waypoints, and the set of position information, absolute altitude information, and relative altitude information determined in step 101 may be used as one waypoint.

Assuming that the flight route of the unmanned aerial vehicle consists of 5 waypoints, namely waypoint 1, waypoint 2, waypoint 3, waypoint 4 and waypoint 5 according to the sequence of time from first to last, firstly, when the unmanned aerial vehicle flies to waypoint 1, determining the position information, the absolute height information and the relative height information of the unmanned aerial vehicle; secondly, when the unmanned aerial vehicle flies to the navigation point 2, determining the position information, the absolute height information and the relative height information of the unmanned aerial vehicle; thirdly, when the unmanned aerial vehicle flies to the navigation point 3, determining the position information, the absolute height information and the relative height information of the unmanned aerial vehicle; then, when the unmanned aerial vehicle flies to the waypoint 4, determining the position information, the absolute height information and the relative height information of the unmanned aerial vehicle; and finally, when the unmanned aerial vehicle flies to the navigation point 5, determining the position information, the absolute height information and the relative height information of the unmanned aerial vehicle.

Therefore, the position information of the unmanned aerial vehicle from the waypoint 1 to the waypoint 5 forms a flight route of the unmanned aerial vehicle, the elevation data of the waypoint 1 in the flight route can be obtained according to the absolute height information and the relative height information of the waypoint 1, the elevation data of the waypoint 2 in the flight route can be obtained according to the absolute height information and the relative height information of the waypoint 2, the elevation data of the waypoint 3 in the flight route can be obtained according to the absolute height information and the relative height information of the waypoint 3, the elevation data of the waypoint 4 in the flight route can be obtained according to the absolute height information and the relative height information of the waypoint 4, and the elevation data of the waypoint 5 in the flight route can be obtained according to the absolute height information and the relative height information of the waypoint 5.

Alternatively, the absolute altitude information minus the relative altitude information for a waypoint may be used as the elevation data for the waypoint. For example, as shown in fig. 2, assuming that the relative height information of the waypoint with respect to the ground feature point P of the ground is h1 and the absolute height information is h2, the elevation data of the waypoint may be equal to h, where h is h2-h 1.

Because the altitude data of the flight route is determined by the unmanned aerial vehicle based on the absolute altitude information, the relative altitude information and the position information, the unmanned aerial vehicle can obtain the absolute altitude information, the relative altitude information and the position information with high precision, and the altitude data of the flight route with high precision can be obtained according to the three types of information. Thus, elevation data of the flight path may be used to assess the accuracy of the ground point cloud map.

And the unmanned aerial vehicle determines the elevation data of the flight route based on the absolute height information, the relative height information and the position information, and does not need to arrange image control points, so that the problem of complex operation caused by the fact that the image control points can be imaged is solved.

It should be noted that the accuracy of the ground point cloud map may be evaluated by the unmanned aerial vehicle according to the altitude data of the flight path of the unmanned aerial vehicle, or may be evaluated by another device other than the unmanned aerial vehicle according to the altitude data of the flight path of the unmanned aerial vehicle.

It should be noted that the ground point cloud map in this embodiment may be a ground point cloud map obtained in any manner.

The method for evaluating the precision of the ground point cloud map provided by the embodiment comprises the steps of determining current position information and absolute height information of an unmanned aerial vehicle through positioning equipment carried by the unmanned aerial vehicle, determining current relative height information of the unmanned aerial vehicle relative to the ground through an airborne radar of the unmanned aerial vehicle, and obtaining altitude data of a flight route of the unmanned aerial vehicle according to the position information, the absolute height information and the relative height information, wherein the altitude data of the flight route is used for evaluating the precision of the ground point cloud map.

Fig. 3 is a schematic flow chart of a ground point cloud map accuracy evaluation method according to another embodiment of the present invention. The present embodiment mainly describes an optional implementation manner after obtaining elevation data of a flight path of the unmanned aerial vehicle on the basis of the embodiment shown in fig. 1. As shown in fig. 3, the method of this embodiment may include:

step 301, determining current position information and absolute height information of the unmanned aerial vehicle through positioning equipment carried by the unmanned aerial vehicle, and determining current relative height information of the unmanned aerial vehicle relative to the ground through an airborne radar of the unmanned aerial vehicle.

It should be noted that step 301 is similar to step 101, and is not described herein again.

Step 302, obtaining elevation data of a flight path of the unmanned aerial vehicle according to the position information, the absolute height information and the relative height information; and the elevation data of the flight route is used for evaluating the precision of the ground point cloud map.

It should be noted that step 302 is similar to step 102, and is not described herein again.

Step 303, calibrating the elevation data in the ground point cloud map according to the elevation data of the flight path.

In this step, optionally, after the elevation data of each waypoint of the flight path is obtained, the elevation data in the ground point cloud map is calibrated; or after the elevation data of all the waypoints of the flight waypoint are obtained, the elevation data in the ground point cloud map can be calibrated.

Optionally, step 303 may specifically include: and taking the elevation data of the flight path as the elevation data after the calibration of the corresponding position of the flight path in the ground point cloud map. Optionally, the position corresponding to the flight path in the ground point cloud map may specifically include: the same position of the waypoint in the ground point cloud map as the position of the waypoint in the flight route, for example, assuming that the position of the flight waypoint is P in fig. 2, the corresponding position in the ground point cloud map includes P. Further optionally, the corresponding position of the flight route in the ground point cloud map may further include: the positions in the ground point cloud map that are adjacent to the positions of waypoints in the flight path, assuming that the positions of the flight waypoints are P in fig. 2, the corresponding positions in the ground point cloud map may include P and P', for example.

For example, if a flight route is composed of waypoints 1 to 5, taking the elevation data of the flight route as the elevation data calibrated at the corresponding position of the flight route in the ground point cloud map may specifically include: the elevation data of the waypoint 1 is used as the elevation data after the calibration of the position corresponding to the waypoint 1 in the ground point cloud map, the elevation data of the waypoint 2 is used as the elevation data after the calibration of the position corresponding to the waypoint 2 in the ground point cloud map, the elevation data of the waypoint 3 is used as the elevation data after the calibration of the position corresponding to the waypoint 3 in the ground point cloud map, the elevation data of the waypoint 4 is used as the elevation data after the calibration of the position corresponding to the waypoint 4 in the ground point cloud map, and the elevation data of the waypoint 5 is used as the elevation data after the calibration of the position corresponding to the waypoint 5 in the ground point cloud map.

Or, optionally, step 303 may specifically include:

determining the calibration quantity of the elevation data of the corresponding position in the ground point cloud map according to the elevation data of the flight route and the elevation data of the corresponding position of the flight route in the ground point cloud map;

and taking the elevation data of the corresponding position in the ground point cloud map and the operation result of the calibration amount as the elevation data after the corresponding position in the ground point cloud map is calibrated.

Optionally, the position corresponding to the flight path in the ground point cloud map may specifically include: the same position of the waypoint in the ground point cloud map as the position of the waypoint in the flight route, for example, assuming that the position of the flight waypoint is P in fig. 2, the corresponding position in the ground point cloud map includes P. Further optionally, the corresponding position of the flight route in the ground point cloud map may further include: the positions in the ground point cloud map that are adjacent to the positions of waypoints in the flight path, assuming that the positions of the flight waypoints are P in fig. 2, the corresponding positions in the ground point cloud map may include P and P', for example.

Optionally, determining a calibration amount of the elevation data of the corresponding position in the ground point cloud map according to the elevation data of the flight route and the elevation data of the corresponding position of the flight route in the ground point cloud map, which may specifically include: when the elevation data of a waypoint of the flight route is the same as the elevation data of the position, which is the same as the waypoint, in the ground point cloud map, the calibration quantity of the elevation data of the position, which corresponds to the waypoint, in the ground point cloud map is equal to 0; and when the elevation data of the waypoint of the flight route is different from the elevation data of the position, which is the same as the waypoint, in the ground point cloud map, taking the calculation result of the elevation data of the waypoint of the flight route and the elevation data of the position, which corresponds to the waypoint, in the ground point cloud map as the calibration quantity of the elevation data of the position, which corresponds to the waypoint, in the ground point cloud map. For example, assuming that the elevation data of a waypoint for a flight point is d1, the elevation data of the same location in the ground point cloud map as the waypoint is d2, and d1 is not equal to d2, the calibration amount may be equal to d1-d2/a, where a is the calibration coefficient and a is a real number greater than 1. It can be seen that the calibration amount may be a positive number when d1 is greater than d2, and a negative number when d1 is less than d 2.

It should be noted that, when there are a plurality of corresponding positions of one waypoint in the ground point cloud map, the calibration amounts of the elevation data of the plurality of positions may be the same or different, and the present invention is not limited thereto.

Optionally, the step of using the elevation data of the corresponding position in the ground point cloud map and the calculation result of the calibration amount as the elevation data after calibration of the corresponding position in the ground point cloud map may specifically include: taking the sum of the elevation data of the corresponding position in the ground point cloud map and the calibration amount as the elevation data after calibration of the corresponding position in the ground point cloud map, for example, when the calibration amount can be equal to d1-d2/a, the sum of the two can be taken as the elevation data after calibration; alternatively, the result of subtracting the calibration amount from the elevation data of the corresponding location in the ground point cloud map is used as the calibrated elevation data of the corresponding location in the ground point cloud map, for example, when the calibration amount may be equal to d2-d1/a, the difference between the two may be used as the calibrated elevation data.

And determining the corrected elevation data of the corresponding position of the flight route in the ground point cloud map according to the elevation data of the flight route, and further optionally correcting the elevation data of other positions except the corresponding position in the ground point cloud map. Optionally, the correcting the elevation data of the other positions except the corresponding position in the ground point cloud map may specifically include: according to the elevation data of each navigation point in the flight route and the elevation data of the corresponding position of each navigation point in the ground point cloud map, determining the calibration quantity of the elevation data of other positions in the ground point cloud map (the calibration quantity may be the same as or different from the calibration quantity of the elevation data of the corresponding position), and taking the calculation result of the calibration quantity and the elevation data of other positions in the ground point cloud map as the calibrated elevation data of other positions in the ground point cloud map.

Optionally, when the accuracy of the ground point cloud map does not meet the accuracy requirement, the elevation data in the ground point cloud map may be calibrated according to the elevation data of the flight route, and when the accuracy of the ground point cloud map meets the accuracy requirement, the elevation data in the ground point cloud map may not be calibrated. Therefore, step 303 may optionally be preceded by: and determining the precision of the ground point cloud map according to the elevation data of the flight route. Optionally, the accuracy of the ground point cloud map may be determined according to the elevation data of at least one waypoint in the flight route and the elevation data of the same position as the at least one waypoint in the ground point cloud map.

For example, the accuracy of the ground point cloud map may be determined from the elevation data of waypoint 1 in the flight path and the elevation data of the same location as waypoint 1 in the ground point cloud map.

As another example, the accuracy of the ground point cloud map may be determined based on the elevation data for waypoint 1 in the flight path and the elevation data for the same location in the ground point cloud map as waypoint 1, the elevation data for waypoint 3 in the flight path and the elevation data for the same location in the ground point cloud map as waypoint 3, and the elevation data for waypoint 5 in the flight path and the elevation data for the same location in the ground point cloud map as waypoint 5.

Specifically, the accuracy of the ground point cloud map may be determined according to the principle that the greater the difference between the elevation data of the waypoint and the elevation data of the waypoint, the lower the accuracy of the ground point cloud map, the smaller the difference between the elevation data of the ground point cloud map and the elevation data of the waypoint, the higher the accuracy of the ground point cloud map. It should be noted that, the present invention is not limited to the specific manner of determining the accuracy of the ground point cloud map according to the elevation data of the waypoint and the elevation data of the same position as the waypoint in the ground point cloud map.

Correspondingly, the calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route specifically comprises:

and if the precision of the ground point cloud map is smaller than a precision threshold value, calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route.

It should be noted that the accuracy threshold may be a preset threshold, or may also be a threshold set by a user, which is not limited in the present invention.

According to the ground point cloud map precision evaluation method provided by the embodiment, the current position information and the absolute height information of the unmanned aerial vehicle are determined through the positioning equipment carried by the unmanned aerial vehicle, the current relative height information of the unmanned aerial vehicle relative to the ground is determined through the airborne radar of the unmanned aerial vehicle, the altitude data of the flight path of the unmanned aerial vehicle is obtained according to the position information, the absolute height information and the relative height information, the altitude data of the flight path is used for evaluating the precision of the ground point cloud map, the altitude data used for evaluating the precision of the ground point cloud map is determined based on the flight of the unmanned aerial vehicle, the operation is simple, and the reliability. In addition, the elevation data in the ground point cloud map is calibrated according to the elevation data of the flight route, so that the accuracy of the elevation data in the ground point cloud map is improved.

Fig. 4 is a schematic flow chart of a ground point cloud map accuracy evaluation method according to another embodiment of the present invention. On the basis of the above method embodiment, a specific implementation manner for determining the current relative height information of the unmanned aerial vehicle with respect to the ground through the airborne radar of the unmanned aerial vehicle is mainly described in this embodiment. As shown in fig. 4, the method of this embodiment may include:

step 401, obtaining first ranging data obtained by ranging the ground by the continuous wave radar in a rotation process, wherein the first ranging data is obtained when a rotation angle of the continuous wave radar is within a preset angle interval.

Step 402, performing clustering processing on the first ranging data, and removing data with clustering density lower than preset density from the first ranging data to obtain second ranging data.

Step 403, determining a terrain parameter of the ground according to the second ranging data, where the terrain parameter includes the relative height information of the ground right below the continuous wave radar distance.

In this embodiment, can carry out the range finding to ground through the continuous wave radar to obtain the distance that this continuous wave radar is apart from ground, wherein the continuous wave radar can rotate, and when the rotatory different angle of continuous wave radar, the range finding point that the continuous wave radar went to the range finding to ground is also different, and consequently the distance that the continuous wave radar detected and ground also can not be the same, as shown in fig. 5. In this embodiment, when the continuous wave radar measures the distance to the ground during the rotation process and the rotation angle of the continuous wave radar is within the preset angle interval, for example, as shown in fig. 6, the preset angle interval is-60 degrees to 60 degrees, where the first distance measurement data includes N data, and N is an integer greater than or equal to 2. Each datum reflects the distance between the continuous wave radar and the ground when the continuous wave radar rotates to a corresponding rotation angle, and for the same ranging point, if the ground where the ranging point is located is high, the distance between the continuous wave radar and the ground is low, and if the ground where the ranging point is located is low, the distance between the continuous wave radar and the ground is large; for example: if the distance difference between the continuous wave radar and different ranging points on the ground is large, the flatness of the ground is low. For the plurality of distance measuring points, if the distances between the continuous wave radar and the ground are all small, the slope of the ground where the plurality of distance measuring points are located is high, and if the distances between the continuous wave radar and the ground are all large, the slope of the ground where the plurality of distance measuring points are located is low.

However, in practical situations, due to interference of the internal and external environments of the continuous wave radar, outliers exist in the distance measured by the continuous wave radar, for example: for the same distance measuring point, the distance between the distance measuring point and the continuous wave radar is larger in practice, but the continuous wave radar is interfered, so that the data obtained by distance measurement is smaller, and the predicted slope of the terrain and the actual slope have larger errors. Especially in complex application scenarios such as farmland, tea mountain and the like, the presence of outliers can lead to inaccurate terrain prediction.

Therefore, in this embodiment, the outlier needs to be removed from the first ranging data, specifically, the embodiment performs clustering processing on the first ranging data, and removes data with a clustering density lower than a preset density from the first ranging data, where the data with the clustering density lower than the preset density can be regarded as the outlier, so as to obtain the second ranging data, where M is a positive integer less than or equal to N. And then determining the terrain parameters of the ground where the plurality of ranging points are located according to the second ranging data obtained after the outliers are removed. Optionally, the terrain parameter may further include: the slope of the ground, the flatness of the ground, etc.

For example: the preset angle interval is-60 degrees to 60 degrees, and correspondingly, the topographic parameters of the ground right below the continuous wave radar can be determined, it should be noted that this is for illustration and is not limited to the present embodiment, and the preset angle interval may be set according to actual needs. If the preset angle interval of this embodiment is-60 degrees to 60 degrees, this embodiment may obtain data by ranging the ground when the rotation angle of the continuous wave radar is 60 degrees, obtain data by ranging the ground at 59.4 degrees, obtain data by ranging the ground at 58.8 degrees, obtain data by ranging the ground at 58.2 degrees, and so on, which is not described herein again, thereby obtaining the first ranging data.

In the embodiment, first ranging data obtained by ranging the ground within a range of a preset angle after rotating in the rotating process are obtained, then the first ranging data are subjected to clustering processing, data with clustering density lower than the preset density are removed from the first ranging data, second ranging data are obtained, and topographic parameters of the ground are determined according to the second ranging data. Because this embodiment is rejected earlier as wild value with the data that cluster density is less than preset density, then carries out the topography prediction, so eliminated the interference that continuous wave radar received for the prediction accuracy of continuous wave radar to ground topography is higher.

Optionally, each data comprises: the rotation angle of the continuous wave radar and the distance between the rotation angle and a ground ranging point. In this embodiment, the continuous wave radar triggers ranging once every time the continuous wave radar rotates to one grating grid, and moreover, every time the continuous wave radar rotates by 0.6 degrees, it means that the continuous wave radar rotates to one corresponding grating grid. Therefore, when the continuous wave radar is triggered to measure distance every time the continuous wave radar rotates by 0.6 degrees, the distance between the continuous wave radar and the ground distance measuring point is obtained, and the rotating angle corresponding to the distance obtained by the continuous wave radar is recorded.

Optionally, each data comprises: the horizontal distance of the continuous wave radar from the ground ranging point, and the vertical distance of the continuous wave radar from the ground ranging point. Because the rotation angles of the continuous wave radar are different, the signal transmitting directions of the continuous wave radar are different, and therefore the ground ranging points are different, and the ground ranging points are different along with the rotation angles of the continuous wave radar. In this embodiment, in order to avoid the situation that the subsequent predicted terrain is inaccurate due to the fact that the distance values between the continuous wave radar and the ground ranging points are the same but the terrain on the ground is different, the data in this embodiment may include the horizontal distance and the vertical distance, where the horizontal distance and the vertical distance may be obtained according to the distance between the continuous wave radar and the ground ranging points and the rotation angle of the continuous wave radar corresponding to the ground ranging points. For example: for the same distance between the continuous wave radar and the ground ranging point, if the horizontal distance between the continuous wave radar and the ground ranging point is larger and the vertical distance is smaller, the slope of the ground can be considered to be higher, and if the horizontal distance between the continuous wave radar and the ground ranging point is smaller and the vertical distance is larger, the slope of the ground can be considered to be lower.

In some embodiments, a possible implementation manner of the step 401 may include the following steps a and B;

and step A, acquiring third ranging data of the continuous wave radar for ranging to the ground in the rotating process.

In this embodiment, all the ranging data obtained when the continuous wave radar measures the distance to the ground during the rotation process and the rotation angle of the continuous wave radar is within the preset angle interval are obtained, where the ranging data are referred to as third ranging data, and the third ranging data include H data, where H is an integer greater than or equal to N.

In some embodiments, one possible implementation manner of step a may include: step a1 and step a 2.

And A1, acquiring all data of ranging to the ground by the continuous wave radar rotating for one circle and the rotating angle of the continuous wave radar corresponding to each second ranging data.

And A2, acquiring data corresponding to the rotation angle of the continuous wave radar in the preset angle interval as third ranging data according to the preset angle interval.

In this embodiment, the continuous wave radar rotates by one circle, and the corresponding continuous wave radar rotates by 360 degrees. For example: the method includes that a continuous wave radar rotates for a circle and corresponds to 600 grating grids, each time the continuous wave radar rotates for 0.6 degrees, the continuous wave radar rotates to one corresponding grating grid, then one-time ranging is triggered, 600 ranging data can be obtained, and in addition, the rotation angle of the continuous wave radar corresponding to each ranging data is recorded; the ranging principle of the continuous wave radar can be referred to the related description in the prior art, and is not described herein again. Then, according to a preset angle interval, data correspondingly obtained when the rotation angle of the continuous wave radar is within the preset angle interval is obtained, for example: if the preset angle interval is-60 to 60 degrees, the corresponding data of-60, -59.4, 58.8, …, 58.8, 59.4 and 60 degrees can be screened out, and 200 data can be obtained.

And B, acquiring the first ranging data according to the third ranging data.

In this embodiment, the third ranging data is data obtained by actual ranging of a continuous wave radar, and after the third ranging data is obtained, the first ranging data is obtained according to the third ranging data.

In some embodiments, one possible implementation of step B above may include step B1.

And step B1, determining the first ranging data according to the third ranging data and effective ranging conditions. Wherein, the effective ranging condition includes: less than or equal to a preset maximum distance and greater than or equal to a preset minimum distance.

In this embodiment, the validity of the data obtained by each ranging is determined, and the continuous wave radar has a blind area in a short-distance range and a farthest ranging distance, so that an effective ranging condition is set, which may be represented as [ d_min,d_max]I.e. data indicating valid ranging should be greater than or equal to d_minAnd is less than or equal to d_max. Therefore, in this embodiment, the first ranging data is determined according to the third ranging data and the effective ranging condition, so that the error of the ranging data is avoided, and the accuracy of the ground terrain prediction is improved.

In some embodiments, one possible implementation of the step B1 described above may include steps B11 and B12.

And step B11, determining N data meeting the effective ranging conditions from the third ranging data.

In this embodiment, all distances that are less than or equal to the preset maximum distance and less than or equal to the preset minimum distance are determined from the third ranging data, and the data including these distances are the N data in the third ranging data.

And step B12, determining the first ranging data according to N data in the third ranging data.

In this embodiment, the first ranging data is determined according to the N pieces of data in the determined third ranging data that satisfy the effective ranging condition.

In one possible implementation, N data of the third ranging data may be determined as the first ranging data.

In another possible implementation manner, N pieces of data in the third ranging data are smoothed to obtain the first ranging data. For example: sequencing N data in the third ranging data according to the sequence of the rotation angles of the continuous wave radar, such as: the 1 st datum is: distance d corresponding to 60 degrees₁And the 2 nd data is: distance d corresponding to 59.4 degrees₂And so on; then, the 1 st data in the third ranging data is determined as the 1 st data in the first ranging data, namely, the distance d corresponding to-60 degrees₁And determining the Nth data in the third ranging data as the Nth data in the first ranging data, namely the distance d corresponding to 60 degrees_N. And determining the (j-1) th data (e.g., d) in the third ranging data_j-1) J-th data (e.g., d) of the third ranging data_j) The (j + 1) th data (e.g., d) of the third ranging data_j+1) The average value of the three is the distance D in the jth data in the first ranging data_jWherein j is an integer of 2 or more and N-1 or less. I.e. D_j＝[d_j-1+d_j+d_j+1]/3。

In addition, D is_jNor to d_jAnd one (i.e., three) adjacent to each other left and rightThose) may be d_jAnd the average values of two (namely five) adjacent left and right, correspondingly, the 1 st data and the 2 nd data in the third ranging data are respectively equal to the 1 st data and the 2 nd data in the first ranging data, and the N-1 st data and the N-1 th data in the third ranging data are respectively equal to the N-1 th data and the N-th data in the first ranging data. In addition, three, four, etc. adjacent to each other on the left and right can be adopted in this embodiment, and the schemes are similar and will not be described herein again.

On the basis of the foregoing embodiments, in some embodiments, a possible implementation manner of the foregoing step 402 may include the following steps 4021 to 4025, as shown in fig. 7.

Step 4021, performing coordinate conversion on the first ranging data to obtain coordinates corresponding to the N data.

In this embodiment, coordinate conversion is performed on the first ranging data to obtain a coordinate corresponding to each data of the N data.

In some embodiments, a rectangular coordinate system may be established with the rotation center of the continuous wave radar as an origin, the direction just before the rotation of the continuous wave radar as the positive direction of the x axis, and the vertically downward direction as the positive direction of the y axis; and then, according to the rectangular coordinate system, performing coordinate conversion on each datum in the first ranging data to obtain a coordinate corresponding to each datum.

Wherein, if each of the first ranging data includes: the rotation angle of the continuous wave radar and the distance between the rotation angle and a ground ranging point. The rotation angle of the continuous wave radar can be represented by corresponding grating scales, and each datum is converted into a coordinate (including a horizontal coordinate and a vertical coordinate) in the rectangular coordinate system after coordinate conversion, wherein the coordinate is as follows:

x_i＝L_i*sin((G₀–G_i)/Z)

y_i＝L_i*cos((G₀–G_i)/Z)

wherein G is₀Is the grating scale under the continuous wave radar, and Z is the angle corresponding to a single grating gridValue (e.g. 0.6 degree), G_iGrating scale values corresponding to the angle of rotation of a continuous wave radar, L_iFor rotating the continuous wave radar to a grating scale value G_iThe corresponding distance.

Step 4022, mapping the coordinates of the N data to a predetermined first matrix.

In this embodiment, after the coordinates of each data are obtained, the coordinates of the N data are mapped to a predetermined first matrix. Alternatively, the horizontal coordinate of the coordinate of each data corresponds to a row number in the first matrix, and the vertical coordinate of the coordinate of each data corresponds to a column number of the first matrix.

Alternatively, the first matrix may be a null matrix, i.e. each matrix element in the first matrix is 0.

Optionally, the embodiment further determines the first matrix in advance, where one possible implementation manner is: determining the number of columns of the first matrix according to the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar; determining the number of rows of the first matrix according to the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar; and then determining a first matrix according to the determined row number and column number.

Since the continuous wave radar can detect the front-back and up-down directions, the horizontal direction detection range of the continuous wave radar in the rectangular coordinate system is [ -L ]_x，L_x]The vertical direction detection range in the above rectangular coordinate system is [ -L ]_y，L_y]Therefore, the maximum horizontal distance detected by the continuous wave radar is L_xThe maximum vertical distance detected by the continuous wave radar is L_y。

The number of rows I and the number of columns J of the first matrix may be, for example:

wherein r is the resolution of the detection distance of the continuous wave radar L_xMaximum horizontal distance detected for the continuous wave radar, L_yThe maximum vertical distance detected for the continuous wave radar.

Therefore, the first determined matrix may be, for example, an empty matrix of I x J, i.e. a matrix with a high degree of freedom

As shown in fig. 8A.

In some embodiments, the mapping of each data to a column position in the first matrix may be determined according to a horizontal coordinate in coordinates of each data, a maximum horizontal distance detected by the continuous wave radar, and a resolution of the continuous wave radar detected distance. And determining the row position of each data mapped to the first matrix according to the vertical coordinate in the coordinate of each data, the maximum vertical distance detected by the continuous wave radar and the resolution of the continuous wave radar detection distance.

Since in this embodiment, the horizontal coordinate or the vertical coordinate of the data may have a negative value, and the row number or the column number of the first matrix has a positive value, this embodiment also needs to determine the column position (i.e., the column number) according to the maximum horizontal distance detected by the continuous wave radar, and also needs to determine the row position (i.e., the row number) according to the maximum vertical distance detected by the continuous wave radar.

Wherein the coordinate corresponding to the data i in the first ranging data is (x)_i，y_i) Mapped to row positions I in the first matrix_iMapped to column position J in the first matrix_i。

Using the first matrix as

For example, since the continuous wave radar is used as the origin (0, 0) of the rectangular coordinate system, the coordinates of the continuous wave radar are mapped to the center of the first matrix, for example, as shown in fig. 8A.

Step 4023, mapping each coordinate to a position in the first matrix, setting a matrix element of the position in the first matrix as a first preset element, and obtaining a second matrix.

In this embodiment, a position in the first matrix is mapped according to each coordinate, and a matrix element of the position in the first matrix is set as a first preset element. For example: taking one of the coordinates as an example, the coordinate is mapped to the 10 th row and the 11 th column in the first matrix, and then the matrix element of the 10 th row and the 11 th column in the first matrix is set as a first preset element. It should be noted that the matrix elements in the first matrix are different from the first preset elements. Taking the first matrix as an empty matrix as an example, the first predetermined element is, for example, 1. Through the above-described processing of the first matrix, a second matrix is obtained, for example, as shown in fig. 8B.

Step 4024, performing clustering operation on the second matrix according to a preset clustering sliding window, and removing matrix elements with clustering density lower than preset density from the second matrix to obtain M first preset elements.

In this embodiment, the second matrix is clustered according to a preset cluster sliding window, and matrix elements with a cluster density lower than a preset density are removed from the second matrix, for example, first preset elements with a cluster density lower than a preset density are also removed from the second long matrix, so as to obtain M first preset elements.

In a possible implementation manner, a sliding window operation is performed on the second matrix according to a preset cluster sliding window size, and a matrix element corresponding to a preset anchor point in each cluster sliding window is obtained, where as shown in fig. 8C, the preset cluster sliding window size is 3, for example, the preset anchor point is, for example, a center in the cluster sliding window in fig. 8C, but the embodiment is not limited thereto. Then, according to a cluster sliding window with the matrix element corresponding to the preset anchor point as a first preset element, matrix elements with the cluster density lower than the preset density are removed from the second matrix, and the M first preset elements are obtained. For example, the matrix element corresponding to the preset anchor point shown in fig. 8C is the first preset element.

Optionally, one implementation manner of obtaining M first preset elements is as follows: and aiming at each cluster sliding window, if the matrix element corresponding to the preset anchor point in the cluster sliding window is a first preset element, acquiring the number of the first preset elements in the cluster sliding window. And judging whether the number of first preset elements in the cluster sliding window is smaller than a first preset value or not, if so, changing the matrix elements corresponding to the preset anchor points in the cluster sliding window into second preset elements (for example, changing from 1 to 0), and if the number of the first preset elements is larger than or equal to the first preset value, keeping the matrix elements corresponding to the preset anchor points in the cluster sliding window as the first preset elements. And then determining all the first preset elements in the second matrix after the change processing as the M first preset elements.

Optionally, when performing a sliding window operation on the second matrix, a start anchor point and an end anchor point of a preset cluster sliding window may be determined from the second matrix, as shown in fig. 8C; and then, starting from the initial anchor point, performing sliding window operation on the second matrix according to the preset size of the cluster sliding window, and stopping the sliding window operation until the termination anchor point is reached. Therefore, sliding window operation is not needed to be carried out on the whole second matrix, the process of sliding window operation can be simplified, and the efficiency of determining the M first preset elements is improved.

The initial anchor point is the position with the minimum row number and the minimum column number of the first preset element in the second matrix, and the termination anchor point is the position with the maximum row number and the maximum column number of the first preset element in the second matrix.

Step 4025, obtaining the second ranging data from the first ranging data according to the M first preset elements.

In this embodiment, according to the M determined first preset elements, second ranging data is obtained from the first ranging data. Wherein, in one possible implementation mode, the method comprises the following steps:

the positions of the M first preset elements in the second matrix are known, so that M coordinates respectively corresponding to the M first preset elements are determined according to the positions of the M first preset elements in the second matrix, for example: the position of one preset element in the second matrix is (I)_i，J_i) For example, the corresponding coordinate is (x)_i，y_i)。

x_i＝J_i*r-L_x

y_i＝I_i*r-L_y

Then, according to the M coordinates, determining data respectively corresponding to the M coordinates from the first ranging data as the second ranging data. That is, M pieces of data for obtaining the above M coordinates through coordinate conversion may be determined as the second ranging data.

On the basis of the foregoing embodiments, in some embodiments, after the second ranging data is obtained, it is determined whether a value of the number M of data included in the second ranging data is smaller than a first preset value, if the value of M is greater than or equal to the first preset value, it is determined that the second ranging data has a sufficient data volume for performing terrain prediction, and then a terrain parameter of the ground is determined according to the second ranging data. If the value of M is smaller than the first preset value, it indicates that the second ranging data is not enough for terrain prediction, and in order to avoid inaccurate terrain prediction, the embodiment determines that the ranging data detected by the continuous wave radar is invalid.

On the basis of the foregoing embodiments, in some embodiments, one possible implementation manner of determining the terrain parameter of the ground according to the second ranging data may include the following steps C and D;

and C, performing straight line fitting according to the second distance measurement data to obtain a second straight line function.

In this embodiment, each data in the second ranging data may be converted into the rectangular coordinate system to obtain a coordinate of each data, and then the coordinates of the M data are subjected to straight line fitting by a least square method to obtain a straight line function.

Wherein a straight-line function of the vertical coordinate with respect to the horizontal coordinate in the coordinates of each datum is constructed, for example, as shown in formula one: and y is ax + b, wherein y is a vertical coordinate in the coordinate of each piece of the second ranging data, and x is a horizontal coordinate in the coordinate of each piece of the second ranging data, and a and b are temporarily unknown. And then determining the slope and intercept of the straight line function according to the second ranging data, the straight line function and a least square method. Wherein the second range data are known and the coordinates of each data in each second range data comprise horizontal and vertical coordinates, substituting the M sets of known values of x and y into the above equation one, and determining the slope (e.g., a) and intercept (e.g., b) of the straight line function by least squares.

Alternatively, a and b above may be determined by the claimer method, as shown below, wherein (x)_i，y_i) The coordinates of any one of the second ranging data.

In this embodiment, the least square method is not limited to the above, and a filtering method may be used.

And D, determining the terrain parameters of the ground according to the straight line function.

If the terrain parameter of the ground includes a slope of the ground, the present embodiment may determine the slope of the ground according to a slope of the straight-line function, for example: the greater the slope, the greater the slope of the ground, and the smaller the slope, the smaller the slope of the ground. Alternatively, the arctan value of the slope may be determined as the slope of the ground.

Optionally, the slope of the ground may be used to guide the drone for subsequent actions to be taken.

If the terrain parameters of the ground include: the height value of the continuous wave radar from the ground right below is determined according to the intercept of the linear function, for example, the intercept of the linear function may be determined as the height value of the continuous wave radar from the ground right below.

Optionally, the height value of the continuous wave radar from the ground right below may be used for obstacle avoidance of the drone, for example: in order to avoid colliding ground crops, in addition, can also be used to unmanned aerial vehicle accuracy and spray because when spraying, need decide the height and spray.

If the terrain parameter of the ground includes the flatness of the ground, the embodiment may determine a residual error in the second linear function corresponding to each data in the second ranging data according to the second ranging data and the linear function; and then determining the flatness of the ground according to the residual errors in the linear functions respectively corresponding to the second ranging data.

The residual error in the straight-line function corresponding to each datum can be obtained through the following formula.

e_i＝y_i–y_i', wherein e_iIs the residual error, y, in the second linear function corresponding to the ith data in the second ranging data_iIs a secondVertical coordinate, y, of coordinate of ith data in ranging data_i' horizontal coordinate x which is coordinate of ith data in second ranging data_iSubstituting the variable x into a linear function to obtain the value of y, i.e. y_i’＝ax_i+b。

Optionally, in this embodiment, the sum of squares of residuals in the linear functions respectively corresponding to the second ranging data may be determined as the flatness of the ground. If the sum of squares of the residuals is larger, it indicates that the ground is more uneven, and if the sum of squares of the residuals is smaller, it indicates that the ground is more even. For example: the flatness of the ground is as follows:

optionally, after the flatness of the ground is determined, the flatness may be used in a height-fixing and obstacle-avoiding scheme of the unmanned aerial vehicle.

In summary, if the data with the clustering density smaller than the preset density in the first ranging data is not removed, the first ranging data is subjected to least square linear fitting, the obtained fitted linear line is shown in fig. 9A, for example, and the terrain parameters of the ground obtained through the fitted linear line are inaccurate. By adopting the solutions of the embodiments of the present specification, after the data with the clustering density smaller than the preset density in the first ranging data is removed, the least square method straight line fitting is performed according to the second ranging data from which the data are removed, and the obtained fitted straight line is, for example, as shown in fig. 9B, and the terrain parameters of the ground obtained through the fitted straight line are more accurate.

In other embodiments, different from the embodiments, after the first ranging data is obtained, data with a clustering density smaller than a preset density is not removed, but weighted least squares straight line fitting is performed on the first ranging data to obtain a straight line function, and then a terrain parameter of the ground is determined according to the straight line function. Therefore, in the embodiment, the interference of the continuous wave radar when the ranging data is obtained can be eliminated by using the weighted least square method, so that the straight line fitting precision can be improved, and the accuracy of terrain prediction is improved.

Wherein, carrying out weighted least square method straight line fitting on the first distance measurement data, and one possible implementation mode for obtaining the straight line function is as follows:

each piece of the first ranging data is subjected to coordinate conversion to obtain coordinates converted into the rectangular coordinate system, and then a straight line function of a vertical coordinate of the coordinates with respect to a horizontal coordinate is constructed, for example, as shown in formula two: y is ax + b, where y represents the vertical coordinate of the coordinates of each piece of data in the first ranging data, and x represents the horizontal coordinate of the coordinates of each piece of data in the first ranging data, when a, b are temporarily unknown. Then x can be determined according to the first ranging data and the straight line function_iCorresponding to y_i', wherein, y_iIs' x_iThe value of y (i.e. the fitted vertical coordinate) obtained by substituting the variable x into the straight-line function, x_iIs a horizontal coordinate of the ith data in the first ranging data.

After determining a fitted vertical coordinate corresponding to a horizontal distance of a coordinate of each data in the first ranging data, determining a residual error in the straight-line function corresponding to each data; wherein the residual error corresponding to each datum is a function of the slope and intercept in the straight-line function, such as: e ═ y_i-ax_i-b. Then, according to the residual error corresponding to each piece of data and the weighting coefficient of the residual error, determining a weighted square sum of the residual errors corresponding to the N pieces of data, where the weighted square sum of the residual errors is, for example, as shown in formula three:

where Q represents the weighted sum of squares of the residuals, w_iAnd a weighting coefficient representing a corresponding residual of the ith data.

This embodiment determines the value of the slope and the value of the intercept of the straight-line function according to the weighted sum of squares of the residuals after obtaining the weighted sum of squares of the residuals. The method specifically comprises the following steps: determining a value of slope and a value of intercept of the straight-line function according to a first derivative of the weighted sum of squares of the residuals to the slope being equal to a first value and a first derivative of the weighted sum of squares of the residuals to the intercept being equal to a second value.

To minimize the value of Q, the values of a and b are optimized, the first and second values may be set to 0. Accordingly, the first derivative of the weighted sum of squares (Q) of the residuals to the slope (a) is equal to 0 and the first derivative of the weighted sum of squares (Q) of the residuals to the intercept (b) is equal to 0, which can be shown for example by the following equation four:

an estimated value of a can be obtained according to the fourth formula

And the estimated value of b

The formula five is respectively as follows:

the embodiment can be used for

As a straight lineThe value of the slope a of the line function, and

the value of the intercept b as a function of the straight line.

Optionally, if the terrain parameter of the ground surface comprises a slope of the ground surface, determining the slope of the ground surface according to the slope a of the straight-line function.

If the terrain parameters of the ground include: and determining the height value of the continuous wave radar from the ground right below according to the intercept of the linear function.

And if the terrain parameters of the ground comprise the flatness of the ground, determining the flatness of the ground according to the value of Q. For example: the value of a (as described above)

) And the value of b (as described above)

) Substituting into the above formula two, thereby obtaining the value of Q. If the value of Q is larger, the ground is more uneven, and if the value of Q is smaller, the ground is more even.

In an alternative scheme, the present embodiment may be stored in advance as the above formula three and formula five, and the obtained first ranging data is substituted into the formula five stored in advance to obtain

And

according to

The slope of the ground is determined. Then will obtain

And

and substituting the obtained value into a prestored formula III to obtain Q, and determining the flatness of the ground according to the value of Q.

In some embodiments, the weighting coefficients of the residuals corresponding to each datum are all equal, and w is equal even if i has different values_iAre all the same, for example: w is a_iAre all equal to 1. Alternatively, for example: w is a_iAre all equal to 1/N, which means that the sum of the weighting coefficients of the residuals corresponding to said first ranging data is equal to 1.

In some embodiments, since the ranging data obtained by the continuous wave radar ranging has an error that becomes larger as the distance increases, it is necessary to weight-assign the corresponding data according to the rotation angle of the continuous wave radar.

In one possible implementation, the weighting coefficient of the residual error corresponding to each datum is a trigonometric function of the rotation angle of the continuous wave radar corresponding to the datum, for example, as shown in formula six:

wherein k is_midRepresenting the median value, k, of a predetermined angular interval_minRepresents the minimum value, k, of a predetermined angular interval_maxRepresents the maximum value, k, of a predetermined angular interval_iThe rotation angle of the continuous wave radar corresponding to the ith data is shown. For example: the preset angle interval is [ -60 DEG ], 60 DEG]A total of 120 deg. of data, a value of k of 1 for-60 deg., a value of 2 for-59 deg., and so on, where k is_maxIs 120, k_midIs 60 or 61, k_minIs 1.

Optionally, if the sum of the weighting coefficients of the residuals corresponding to the first ranging data is equal to 1, normalization processing needs to be performed on the trigonometric function, and therefore, the weighting coefficient of the residuals is, for example, as shown in formula seven:

in another possible implementation manner, the weighting coefficient of the residual error corresponding to each datum is a gaussian function about the rotation angle of the continuous wave radar corresponding to the datum, for example, as shown in formula eight:

wherein x is_iIs the horizontal coordinate of the ith data in the first ranging data, sigma and mu are constants, and mu represents x₁To x_NMean value of (a)²Denotes x₁To x_NThe variance of (c).

Wherein the shape of the function is adjustable according to the value of the variance; the value of the variance can be preset according to actual needs.

Optionally, if the sum of the weighting coefficients of the residuals corresponding to the first ranging data is equal to 1, normalization processing needs to be performed on the gaussian function, and therefore, the weighting coefficients of the residuals are expressed as formula nine:

in another possible implementation manner, the weighting coefficient of the residual error corresponding to each datum is an error function of the rotation angle of the continuous wave radar corresponding to the datum, for example, as shown in formula ten:

wherein e is_i＝y_i–y_i', wherein e_iIs the residual error, y, in the linear function corresponding to the ith data in the first ranging data_iIs a vertical coordinate, y, of the coordinate of the ith data in the first ranging data_i' horizontal coordinate x which is coordinate of ith data in first ranging data_iSubstituting the variable x into a linear function to obtain the value of y, i.e. y_i’＝ax_i+b。

Wherein, the smaller the error, the larger the weight coefficient; the larger the error, the smaller the weight coefficient.

Optionally, if the sum of the weighting coefficients of the residuals corresponding to the first ranging data is equal to 1, normalization processing needs to be performed on the error function, and therefore, the weighting coefficients of the residuals are, for example, as shown in formula eleven:

fig. 10 is a schematic flow chart of a ground point cloud map accuracy evaluation method according to another embodiment of the present invention. The present embodiment mainly describes an optional implementation manner of the ground point cloud map on the basis of the embodiment shown in fig. 3. As shown in fig. 10, the method of the present embodiment may include:

step 1001, acquiring ground information through an airborne sensor, and drawing a ground point cloud map in real time based on the ground information.

In this step, the onboard sensor may include, for example: any sensor capable of obtaining ground information, such as a binocular vision sensor, a monocular vision sensor, and the like. The ground information may specifically indicate the form of the ground surface and the objects present on the ground surface, and may include, for example, topographic information, building information, vegetation information, river information, and the like. The ground information reflects the forms of the ground surface and objects on the ground surface, so that the ground point cloud map can be drawn in real time according to the ground information and the reference object.

Specifically, the ground point cloud map can be drawn in real time based on ground information obtained through an airborne sensor in the flight process of the unmanned aerial vehicle.

Alternatively, step 1001 may be triggered by a time condition, such as triggering step 1001 to be executed every 0.5 seconds; alternatively, the execution of step 1001 may be triggered by a distance, e.g. every 0.5 meters; alternatively, both time conditions and distance conditions may trigger execution of step 1001, e.g., triggering execution of step 1001 every 0.5 seconds and every 0.5 meters.

Step 1002, determining current position information and absolute height information of the unmanned aerial vehicle through positioning equipment carried by the unmanned aerial vehicle, and determining current relative height information of the unmanned aerial vehicle relative to the ground through an airborne radar of the unmanned aerial vehicle.

The trigger conditions for executing step 1002 may be the same as or different from the trigger conditions for executing step 1001, and the present invention is not limited thereto.

It should be noted that, the related content of step 1002 can refer to the foregoing embodiments, and is not described herein again.

And 1003, obtaining elevation data of the flight path of the unmanned aerial vehicle according to the position information, the absolute height information and the relative height information.

It should be noted that, the related content of step 1003 may refer to the foregoing embodiment, and is not described herein again.

And 1004, calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route.

In this step, optionally, after the ground point cloud map is drawn, the elevation data in the ground point cloud map is calibrated according to the elevation data of the flight route; or, optionally, in the process of drawing the ground point cloud map, calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route, for example, assuming that the waypoints of the flight route include waypoints 1 to waypoints 5 in the order of time from first to last, after obtaining the elevation data of the waypoint 1, calibrating the elevation data of the ground point cloud map including the waypoint 1 and the position before the waypoint 1; after the elevation data of the waypoint 2 is obtained, the elevation data of the positions between the waypoint 1 and the waypoint 2 in the ground point cloud map can be calibrated; after the elevation data of the waypoint 3 is obtained, the elevation data of the positions between the waypoint 2 and the waypoint 3 and including the waypoint 3 in the ground point cloud map can be calibrated; after the elevation data of the waypoint 4 is obtained, the elevation data of the positions between the waypoint 4 and the waypoint 3 and the waypoint 4 in the ground point cloud map can be calibrated; after obtaining elevation data for waypoint 5, the elevation data for the ground point cloud map including waypoint 5 and the locations between waypoints 4 and waypoint 5 may be calibrated.

It should be noted that, the related content of step 1004 can refer to the foregoing embodiments, and is not described herein again.

The ground point cloud map precision evaluation method provided by the embodiment includes the steps that ground information is obtained through an airborne sensor, a ground point cloud map is drawn in real time based on the ground information, the current position information and the absolute height information of an unmanned aerial vehicle are determined through positioning equipment carried by the unmanned aerial vehicle, the current relative height information of the unmanned aerial vehicle relative to the ground is determined through an airborne radar of the unmanned aerial vehicle, the altitude data of a flight route of the unmanned aerial vehicle is obtained according to the position information, the absolute height information and the relative height information, the altitude data in the ground point cloud map is calibrated according to the altitude data of the flight route, the calibration of the altitude data in the ground point cloud map drawn in real time by the unmanned aerial vehicle is achieved, and the precision of the altitude data in the ground point cloud map drawn.

The embodiment of the present invention further provides a computer-readable storage medium, in which program instructions are stored, and when the program is executed, the program may include some or all of the steps of the ground point cloud map precision evaluation method in the above method embodiments.

Embodiments of the present invention provide a computer program, which is used to implement the ground point cloud map accuracy assessment method in any of the above method embodiments when the computer program is executed by a computer.

Fig. 11 is a schematic structural diagram of a ground point cloud map accuracy evaluation system according to an embodiment of the present invention, as shown in fig. 11, a ground point cloud map accuracy evaluation system 1100 according to the embodiment of the present invention may include: a memory 1101 and a processor 1102; the memory 1101 and the processor 1102 are connected by a bus. The memory 1101 may include read-only memory and random access memory, and provides instructions and data to the processor 802. A portion of the memory 1101 may also include non-volatile random access memory.

The memory 1101 is used for storing program codes.

The processor 1102, which invokes the program code, when executed, is configured to:

In one possible implementation, the processor 1102 is further configured to:

and calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route.

In one possible implementation, the processor 1102 is configured to calibrate the elevation data in the ground point cloud map according to the elevation data of the flight path, where the calibrating includes:

and taking the elevation data of the flight path as the elevation data after the calibration of the corresponding position of the flight path in the ground point cloud map.

In one possible implementation, the processor 1102 is configured to calibrate elevation data in the ground point cloud map according to elevation data of a flight path of the unmanned aerial vehicle, and specifically includes:

In one possible implementation, the processor 1102 is further configured to determine an accuracy of the ground point cloud map according to elevation data of the flight path;

the processor 1102 is configured to calibrate elevation data in the ground point cloud map according to elevation data of a flight path of the unmanned aerial vehicle, and specifically includes:

and if the precision of the ground point cloud map is smaller than a precision threshold value, the unmanned aerial vehicle calibrates the elevation data in the ground point cloud map according to the elevation data of the flight path of the unmanned aerial vehicle.

In one possible implementation, the processor 1102 is further configured to obtain ground information through an onboard sensor, and render the ground point cloud map in real time based on the ground information.

In one possible implementation, the positioning device includes a real-time kinematic RTK mobile terminal.

In one possible implementation, the airborne radar comprises a continuous wave radar or a lidar.

In one possible implementation, the airborne radar is a continuous wave radar;

the processor 1102 is configured to determine, through an airborne radar of the drone, current relative height information of the drone with respect to the ground, and specifically includes:

acquiring first ranging data obtained by ranging the ground by a continuous wave radar in a rotating process, wherein the first ranging data comprises N data obtained by the continuous wave radar in a preset rotating angle interval, and N is an integer greater than 1;

clustering the first ranging data, and removing data with clustering density lower than preset density from the first ranging data to obtain second ranging data, wherein the second ranging data comprises M data, and M is a positive integer less than or equal to N;

and determining the terrain parameters of the ground according to the second ranging data, wherein the terrain parameters comprise the relative height information of the ground just below the continuous wave radar distance.

In one possible implementation, each datum comprises:

the rotation angle of the continuous wave radar and the distance between the rotation angle and a ground ranging point;

or the horizontal distance and the vertical distance of the continuous wave radar from the ground ranging point.

In a possible implementation, the processor 1102 is configured to perform clustering on the first ranging data, and remove data with a clustering density lower than a preset density from the first ranging data to obtain second ranging data, and specifically includes:

performing coordinate conversion on the first ranging data to obtain coordinates corresponding to the N data;

mapping the coordinates of the N data to a predetermined first matrix;

mapping each coordinate to a position in the first matrix, and setting a matrix element of the position in the first matrix as a first preset element to obtain a second matrix;

clustering the second matrix according to a preset clustering sliding window, and removing matrix elements with clustering density lower than preset density from the second matrix to obtain M first preset elements;

obtaining the second ranging data from the first ranging data according to the M first preset elements;

wherein matrix elements in the first matrix are different from the first preset elements.

In a possible implementation, the processor 1102 is configured to obtain the second ranging data from the first ranging data according to the M first preset elements, and specifically includes:

determining M coordinates respectively corresponding to the M first preset elements according to the positions of the M first preset elements in the second matrix;

and according to the M coordinates, determining data respectively corresponding to the M coordinates from the first ranging data as the second ranging data.

In a possible implementation, the processor 1102 is configured to perform coordinate transformation on the first ranging data to obtain coordinates corresponding to the N data, and specifically includes:

establishing a rectangular coordinate system by taking the rotation center of the continuous wave radar as an origin, taking the direction right before the rotation of the continuous wave radar as the positive direction of an x axis and taking the vertical downward direction as the positive direction of a y axis;

and performing coordinate conversion on each data in the first ranging data according to the rectangular coordinate system to obtain a coordinate corresponding to each data.

In a possible implementation, the processor 1102 is configured to map the coordinates of the N data into a predetermined first matrix, and specifically includes:

determining a column position of each data mapped to the first matrix according to a horizontal coordinate in the coordinates of each data, a maximum horizontal distance detected by the continuous wave radar, and a resolution of the continuous wave radar detection distance;

and determining the row position of each data mapped to the first matrix according to the vertical coordinate in the coordinate of each data, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.

In a possible implementation, the processor 1102 is configured to determine, according to positions of the M first preset elements in the second matrix, M coordinates respectively corresponding to the M first preset elements, and specifically includes:

for each first preset element, determining a horizontal coordinate in coordinates corresponding to the first preset element according to the row position of the first preset element in the second matrix, the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining a vertical coordinate in the coordinate corresponding to the first preset element according to the column position of the first preset element in the second matrix, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.

In one possible implementation, the processor 1102 is further configured to:

determining the number of rows of the first matrix according to the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

determining the number of columns of the first matrix according to the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining the first matrix according to the row number and the column number.

In one possible implementation, the first matrix is a null matrix.

In a possible implementation, the processor 1102 is configured to perform a clustering operation on the second matrix according to a preset clustering sliding window, and remove matrix elements with a clustering density lower than a preset density from the second matrix to obtain M first preset elements, where the method specifically includes:

performing sliding window operation on the second matrix according to the preset size of the cluster sliding window to obtain matrix elements corresponding to preset anchor points in each cluster sliding window;

and according to a cluster sliding window with the matrix element corresponding to the preset anchor point as a first preset element, eliminating the matrix element with the clustering density lower than the preset density from the second matrix to obtain the M first preset elements.

In a possible implementation, the processor 1102 is configured to, according to a cluster sliding window in which a matrix element corresponding to a preset anchor point is a first preset element, remove a matrix element with a clustering density lower than a preset density from the second matrix, and obtain the M first preset elements, specifically including:

for each cluster sliding window, if a matrix element corresponding to a preset anchor point in the cluster sliding window is a first preset element, acquiring the number of the first preset elements in the cluster sliding window;

if the number of the first preset elements is smaller than a first preset value, changing matrix elements corresponding to preset anchor points in the cluster sliding window into second preset elements, and if the number of the first preset elements is larger than or equal to the first preset value, keeping the matrix elements corresponding to the preset anchor points in the cluster sliding window as the first preset elements;

determining all the first preset elements in the second matrix after the change processing to be the M first preset elements.

In a possible implementation, the processor 1102 is configured to perform a sliding window operation on the second matrix according to a preset sliding window size of a cluster, and specifically includes:

determining a starting anchor point and a terminating anchor point of a preset cluster sliding window from the second matrix;

and starting from the initial anchor point, performing sliding window operation on the second matrix according to the preset size of the cluster sliding window, and stopping the sliding window operation until the termination anchor point is reached.

In a possible implementation, the start anchor point is a position where a row number of the first preset element in the second matrix is the smallest and a column number of the first preset element in the second matrix is the smallest, and the end anchor point is a position where a row number of the first preset element in the second matrix is the largest and a column number of the first preset element in the second matrix is the largest.

In a possible implementation, the processor 1102 is configured to determine the terrain parameter of the ground according to the second ranging data, and specifically includes:

and when the number M of the data included in the second ranging data is larger than or equal to a second preset value, determining the terrain parameters of the ground according to the second ranging data.

performing linear fitting according to the second ranging data to obtain a linear function;

and determining the terrain parameters of the ground according to the straight line function.

In a possible implementation, the processor 1102 is configured to determine the terrain parameter of the ground according to the straight-line function, and specifically includes:

and determining the height value of the continuous wave radar from the ground right below according to the intercept in the linear function.

In a possible implementation, the processor 1102 is configured to acquire first ranging data for a continuous wave radar to range to the ground during a rotation process, and specifically includes:

acquiring third ranging data of the continuous wave radar for ranging the ground in the rotation process; the third ranging data comprise H data, the H data are all data for ranging the ground when the rotation angle of the continuous wave radar is within a preset angle interval, and H is an integer greater than or equal to N;

and acquiring the first ranging data according to the third ranging data.

In a possible implementation, the processor 1102 is configured to obtain the first ranging data according to the third ranging data, and specifically includes:

determining the first ranging data according to the third ranging data and an effective ranging condition;

wherein, the effective ranging condition includes: less than or equal to a preset maximum distance and greater than or equal to a preset minimum distance.

In a possible implementation, the processor 1102 is configured to determine the first ranging data according to the third ranging data and a valid ranging range, and specifically includes:

determining N data satisfying the effective ranging condition from the third ranging data;

and determining the first ranging data according to N data in the third ranging data.

In a possible implementation, the processor 1102 is configured to determine the first ranging data according to N data in the third ranging data, and specifically includes:

determining N data in the third ranging data as the first ranging data; or,

and performing smoothing processing on the N data in the third ranging data to obtain the first ranging data.

In one possible implementation, the processor 1102 is configured to perform smoothing on N pieces of data in the third ranging data to obtain the first ranging data, and includes:

sequencing N data in the third ranging data according to the sequence of the rotation angles of the continuous wave radar;

determining that the 1 st data in the sequenced third ranging data is the 1 st data in the first ranging data, and the Nth data in the sequenced third ranging data is the Nth data in the first ranging data;

determining the average value of the j-1 th data, the j-1 th data and the j +1 th data in the sequenced third ranging data as the j-th data in the first ranging data;

wherein j is an integer of 2 or more and N-1 or less.

In one possible implementation, the acquiring third ranging data for the continuous wave radar to range to the ground during the rotation includes:

acquiring all data of ground ranging of a continuous wave radar rotating for one circle and a rotation angle of the continuous wave radar corresponding to each data;

and acquiring data corresponding to the rotation angle of the continuous wave radar within the preset angle interval as third ranging data according to the preset angle interval.

The ground point cloud map precision evaluation system provided by this embodiment may be used to implement the technical scheme of the above method embodiment of the present invention, and the implementation principle and technical effect are similar, which are not described herein again.

Fig. 12 is a schematic structural diagram of a ground point cloud map precision evaluation apparatus according to an embodiment of the present invention, and as shown in fig. 12, the ground point cloud map precision evaluation apparatus 1200 according to the embodiment includes: positioning device 1201, airborne radar 1202, and ground point cloud map accuracy evaluation system 1203. The ground point cloud map precision evaluation system 1203 may adopt the structure of the embodiment shown in fig. 11, and accordingly, the technical solutions of the above method embodiments may be implemented, and the implementation principle and the technical effect are similar, which are not described herein again.

Fig. 13 is a schematic structural diagram of an unmanned aerial vehicle according to an embodiment of the present invention, and as shown in fig. 13, an unmanned aerial vehicle 1300 according to this embodiment includes: positioning device 1301, airborne radar 1302, and processor 1303. The processor 1303 is configured to determine, through the positioning device 1301, current position information and absolute height information of the unmanned aerial vehicle;

the processor 1303 is further configured to determine, through the airborne radar 1302, information about the current relative height of the drone with respect to the ground;

the processor 1303 is further configured to obtain elevation data of a flight path of the unmanned aerial vehicle according to the position information, the absolute altitude information, and the relative altitude information; and the altitude data of the flight path of the unmanned aerial vehicle is used for evaluating the precision of the ground point cloud map.

In one possible implementation, the processor 1303 is further configured to:

In a possible implementation, the processor 1303 is configured to calibrate the elevation data in the ground point cloud map according to the elevation data of the flight path, and specifically includes:

In a possible implementation, the processor 1303 is configured to calibrate elevation data in the ground point cloud map according to elevation data of a flight path of the unmanned aerial vehicle, and specifically includes:

In a possible implementation, the processor 1303 is further configured to determine the accuracy of the ground point cloud map according to the elevation data of the flight path;

the processor 1303 is configured to calibrate elevation data in the ground point cloud map according to elevation data of a flight path of the unmanned aerial vehicle, and specifically includes:

In a possible implementation, the processor 1303 is further configured to obtain ground information through an onboard sensor, and draw the ground point cloud map in real time based on the ground information.

In one possible implementation, the airborne radar is a continuous wave radar;

the processor 1303 is configured to determine, through an airborne radar of the drone, information of a current relative height of the drone with respect to the ground, and specifically includes:

In one possible implementation, each datum comprises:

In a possible implementation, the processor 1303 is configured to perform clustering on the first ranging data, and remove data with a clustering density lower than a preset density from the first ranging data to obtain second ranging data, and specifically includes:

mapping the coordinates of the N data to a predetermined first matrix;

In a possible implementation, the processor 1303 is configured to obtain the second ranging data from the first ranging data according to the M first preset elements, and specifically includes:

In a possible implementation, the processor 1303 is configured to perform coordinate transformation on the first ranging data to obtain coordinates corresponding to the N data, and specifically includes:

In a possible implementation, the processor 1303 is configured to map the coordinates of the N data into a predetermined first matrix, and specifically includes:

In a possible implementation, the processor 1303 is configured to determine, according to positions of the M first preset elements in the second matrix, M coordinates respectively corresponding to the M first preset elements, and specifically includes:

In one possible implementation, the processor 1303 is further configured to:

In one possible implementation, the first matrix is a null matrix.

In a possible implementation, the processor 1303 is configured to perform a clustering operation on the second matrix according to a preset clustering sliding window, and remove matrix elements with a clustering density lower than a preset density from the second matrix to obtain M first preset elements, where the method specifically includes:

In a possible implementation, the processor 1303 is configured to, according to a cluster sliding window in which a matrix element corresponding to a preset anchor point is a first preset element, remove a matrix element with a clustering density lower than a preset density from the second matrix, and obtain the M first preset elements, specifically including:

In a possible implementation, the processor 1303 is configured to perform a sliding window operation on the second matrix according to a preset sliding window size of a cluster, and specifically includes:

In a possible implementation, the processor 1303 is configured to determine a terrain parameter of the ground according to the second ranging data, and specifically includes:

In a possible implementation, the processor 1303 is configured to determine a terrain parameter of the ground according to the straight-line function, and specifically includes:

In a possible implementation, the processor 1303 is configured to obtain first ranging data for the continuous wave radar to range the ground during a rotation process, and specifically includes:

and acquiring the first ranging data according to the third ranging data.

In a possible implementation, the processor 1303 is configured to obtain the first ranging data according to the third ranging data, and specifically includes:

In a possible implementation, the processor 1303 is configured to determine the first ranging data according to the third ranging data and an effective ranging range, and specifically includes:

In a possible implementation, the processor 1303 is configured to determine the first ranging data according to N data in the third ranging data, and specifically includes:

determining N data in the third ranging data as the first ranging data; or,

In a possible implementation, the processor 1303 is configured to perform smoothing processing on N pieces of data in the third ranging data to obtain the first ranging data, and includes:

wherein j is an integer of 2 or more and N-1 or less.

The unmanned aerial vehicle provided by the embodiment can be used for executing the technical scheme of the method embodiment of the invention, the implementation principle and the technical effect are similar, and the details are not repeated here.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

A method for evaluating the accuracy of a ground point cloud map is characterized by comprising the following steps:

determining current position information and absolute height information of the unmanned aerial vehicle through positioning equipment carried by the unmanned aerial vehicle, and determining current relative height information of the unmanned aerial vehicle relative to the ground through an airborne radar of the unmanned aerial vehicle;

acquiring altitude data of a flight path of the unmanned aerial vehicle according to the position information, the absolute altitude information and the relative altitude information; and the elevation data of the flight route is used for evaluating the precision of the ground point cloud map.
The method of claim 1, further comprising:

and calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route.
The method of claim 2, wherein calibrating the elevation data in the ground point cloud map according to the elevation data of the flight path comprises:

and taking the elevation data of the flight path as the elevation data after the calibration of the corresponding position of the flight path in the ground point cloud map.
The method of claim 2, wherein calibrating the elevation data in the ground point cloud map according to the elevation data of the flight path comprises:

determining the calibration quantity of the elevation data of the corresponding position in the ground point cloud map according to the elevation data of the flight route and the elevation data of the corresponding position of the flight route in the ground point cloud map;

and taking the elevation data of the corresponding position in the ground point cloud map and the operation result of the calibration amount as the elevation data after the corresponding position in the ground point cloud map is calibrated.
The method according to any one of claims 2-4, further comprising: determining the precision of the ground point cloud map according to the elevation data of the flight route;

according to the elevation data of the flight route, calibrating the elevation data in the ground point cloud map, including:

and if the precision of the ground point cloud map is smaller than a precision threshold value, calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route.
The method according to any one of claims 1-5, further comprising:

and acquiring ground information through an airborne sensor, and drawing the ground point cloud map in real time based on the ground information.
The method according to any of claims 1-6, characterized in that the positioning device comprises a real time kinematic RTK mobile terminal.
The method according to any of claims 1-7, wherein the airborne radar comprises a continuous wave radar or a lidar.
The method of claim 8, wherein the airborne radar is a continuous wave radar;

the determining, by an airborne radar of the drone, current relative altitude information of the drone relative to the ground includes:

acquiring first ranging data obtained by ranging the ground by a continuous wave radar in a rotating process, wherein the first ranging data comprises N data obtained by the continuous wave radar in a preset rotating angle interval, and N is an integer greater than 1;

clustering the first ranging data, and removing data with clustering density lower than preset density from the first ranging data to obtain second ranging data, wherein the second ranging data comprises M data, and M is a positive integer less than or equal to N;

and determining the terrain parameters of the ground according to the second ranging data, wherein the terrain parameters comprise the relative height information of the ground just below the continuous wave radar distance.
The method of claim 9, wherein each datum comprises:

the rotation angle of the continuous wave radar and the distance between the rotation angle and a ground ranging point;

or the horizontal distance and the vertical distance of the continuous wave radar from the ground ranging point.
The method of claim 10, wherein clustering the first ranging data, and removing data with a clustering density lower than a preset density from the first ranging data to obtain second ranging data comprises:

performing coordinate conversion on the first ranging data to obtain coordinates corresponding to the N data;

mapping the coordinates of the N data to a predetermined first matrix;

mapping each coordinate to a position in the first matrix, and setting a matrix element of the position in the first matrix as a first preset element to obtain a second matrix;

clustering the second matrix according to a preset clustering sliding window, and removing matrix elements with clustering density lower than preset density from the second matrix to obtain M first preset elements;

obtaining the second ranging data from the first ranging data according to the M first preset elements;

wherein matrix elements in the first matrix are different from the first preset elements.
The method according to claim 11, wherein said obtaining the second ranging data from the first ranging data according to the M first preset elements comprises:

determining M coordinates respectively corresponding to the M first preset elements according to the positions of the M first preset elements in the second matrix;

and according to the M coordinates, determining data respectively corresponding to the M coordinates from the first ranging data as the second ranging data.
The method according to claim 11 or 12, wherein performing coordinate transformation on the first ranging data to obtain coordinates corresponding to the N data comprises:

establishing a rectangular coordinate system by taking the rotation center of the continuous wave radar as an origin, taking the direction right before the rotation of the continuous wave radar as the positive direction of an x axis and taking the vertical downward direction as the positive direction of a y axis;

and performing coordinate conversion on each data in the first ranging data according to the rectangular coordinate system to obtain a coordinate corresponding to each data.
The method of claim 11, wherein mapping the coordinates of the N data into a predetermined first matrix comprises:

determining a column position of each data mapped to the first matrix according to a horizontal coordinate in the coordinates of each data, a maximum horizontal distance detected by the continuous wave radar, and a resolution of the continuous wave radar detection distance;

and determining the row position of each data mapped to the first matrix according to the vertical coordinate in the coordinate of each data, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.
The method according to claim 12, wherein the determining M coordinates corresponding to the M first preset elements respectively according to the positions of the M first preset elements in the second matrix comprises:

for each first preset element, determining a horizontal coordinate in coordinates corresponding to the first preset element according to the row position of the first preset element in the second matrix, the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining a vertical coordinate in the coordinate corresponding to the first preset element according to the column position of the first preset element in the second matrix, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.
The method according to any one of claims 11-15, further comprising:

determining the number of rows of the first matrix according to the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

determining the number of columns of the first matrix according to the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining the first matrix according to the row number and the column number.
The method of any of claims 11-16, wherein the first matrix is a null matrix.
The method according to any one of claims 11 to 17,

clustering the second matrix according to a preset clustering sliding window, and removing matrix elements with clustering density lower than preset density from the second matrix to obtain M first preset elements, wherein the method comprises the following steps:

performing sliding window operation on the second matrix according to the preset size of the cluster sliding window to obtain matrix elements corresponding to preset anchor points in each cluster sliding window;

and according to a cluster sliding window with the matrix element corresponding to the preset anchor point as a first preset element, eliminating the matrix element with the clustering density lower than the preset density from the second matrix to obtain the M first preset elements.
The method according to claim 18, wherein the obtaining the M first preset elements by removing, according to a cluster sliding window in which a matrix element corresponding to a preset anchor point is a first preset element, a matrix element with a cluster density lower than a preset density from the second matrix comprises:

for each cluster sliding window, if a matrix element corresponding to a preset anchor point in the cluster sliding window is a first preset element, acquiring the number of the first preset elements in the cluster sliding window;

if the number of the first preset elements is smaller than a first preset value, changing matrix elements corresponding to preset anchor points in the cluster sliding window into second preset elements, and if the number of the first preset elements is larger than or equal to the first preset value, keeping the matrix elements corresponding to the preset anchor points in the cluster sliding window as the first preset elements;

determining all the first preset elements in the second matrix after the change processing to be the M first preset elements.
The method according to claim 18 or 19, wherein the sliding window operation on the second matrix according to a preset cluster sliding window size comprises:

determining a starting anchor point and a terminating anchor point of a preset cluster sliding window from the second matrix;

and starting from the initial anchor point, performing sliding window operation on the second matrix according to the preset size of the cluster sliding window, and stopping the sliding window operation until the termination anchor point is reached.
The method according to claim 20, wherein the start anchor point is a position in the second matrix where the row number and the column number of the first predetermined element are the smallest, and the end anchor point is a position in the second matrix where the row number and the column number of the first predetermined element are the largest.
The method of any one of claims 9 to 21, wherein determining a topographical parameter of the surface from the second ranging data comprises:

and when the number M of the data included in the second ranging data is larger than or equal to a second preset value, determining the terrain parameters of the ground according to the second ranging data.
The method of any one of claims 9 to 22, wherein determining a topographical parameter of the surface from the second ranging data comprises:

performing linear fitting according to the second ranging data to obtain a linear function;

and determining the terrain parameters of the ground according to the straight line function.
The method of claim 23, wherein said determining a terrain parameter of the ground surface from the linear function comprises:

and determining the height value of the continuous wave radar from the ground right below according to the intercept in the linear function.
The method of any one of claims 9 to 24, wherein the obtaining first ranging data for the continuous wave radar to range the ground during rotation comprises:

acquiring third ranging data of the continuous wave radar for ranging the ground in the rotation process; the third ranging data comprise H data, the H data are all data for ranging the ground when the rotation angle of the continuous wave radar is within a preset angle interval, and H is an integer greater than or equal to N;

and acquiring the first ranging data according to the third ranging data.
The method of claim 25, wherein obtaining the first ranging data from the third ranging data comprises:

determining the first ranging data according to the third ranging data and an effective ranging condition;

wherein, the effective ranging condition includes: less than or equal to a preset maximum distance and greater than or equal to a preset minimum distance.
The method of claim 26, wherein determining the first ranging data based on the third ranging data and a valid ranging range comprises:

determining N data satisfying the effective ranging condition from the third ranging data;

and determining the first ranging data according to N data in the third ranging data.
The method of claim 27, wherein determining the first ranging data from N of the third ranging data comprises:

determining N data in the third ranging data as the first ranging data; or,

and performing smoothing processing on the N data in the third ranging data to obtain the first ranging data.
The method of claim 28, wherein the smoothing N data of the third ranging data to obtain the first ranging data comprises:

sequencing N data in the third ranging data according to the sequence of the rotation angles of the continuous wave radar;

determining that the 1 st data in the sequenced third ranging data is the 1 st data in the first ranging data, and the Nth data in the sequenced third ranging data is the Nth data in the first ranging data;

determining the average value of the j-1 th data, the j-1 th data and the j +1 th data in the sequenced third ranging data as the j-th data in the first ranging data;

wherein j is an integer of 2 or more and N-1 or less.
The method of any one of claims 25 to 29, wherein obtaining third ranging data for the continuous wave radar to range the ground during rotation comprises:

acquiring all data of ground ranging of a continuous wave radar rotating for one circle and a rotation angle of the continuous wave radar corresponding to each data;

and acquiring data corresponding to the rotation angle of the continuous wave radar within the preset angle interval as third ranging data according to the preset angle interval.
A system for evaluating accuracy of a ground point cloud map is characterized by comprising: a processor and a memory;

the memory for storing program code;

the processor, invoking the program code, when executed, is configured to:

determining current position information and absolute height information of the unmanned aerial vehicle through positioning equipment carried by the unmanned aerial vehicle, and determining current relative height information of the unmanned aerial vehicle relative to the ground through an airborne radar of the unmanned aerial vehicle;

acquiring altitude data of a flight path of the unmanned aerial vehicle according to the position information, the absolute altitude information and the relative altitude information; and the elevation data of the flight route is used for evaluating the precision of the ground point cloud map.
The system of claim 31, wherein the processor is further configured to:

and calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route.
The system of claim 32, wherein the processor is configured to calibrate elevation data in the ground point cloud map based on elevation data of the flight path, and further comprising:

and taking the elevation data of the flight path as the elevation data after the calibration of the corresponding position of the flight path in the ground point cloud map.
The system of claim 32, wherein the processor is configured to calibrate the elevation data in the ground point cloud map according to the elevation data of the flight path of the drone, and specifically comprises:

determining the calibration quantity of the elevation data of the corresponding position in the ground point cloud map according to the elevation data of the flight route and the elevation data of the corresponding position of the flight route in the ground point cloud map;

and taking the elevation data of the corresponding position in the ground point cloud map and the operation result of the calibration amount as the elevation data after the corresponding position in the ground point cloud map is calibrated.
The system of any one of claims 32-34, wherein the processor is further configured to determine an accuracy of the ground point cloud map based on elevation data of the flight path;

the processor is used for calibrating the elevation data in the ground point cloud map according to the elevation data of the flight path of the unmanned aerial vehicle, and specifically comprises the following steps:

and if the precision of the ground point cloud map is smaller than a precision threshold value, the unmanned aerial vehicle calibrates the elevation data in the ground point cloud map according to the elevation data of the flight path of the unmanned aerial vehicle.
The system of any one of claims 31-35, wherein the processor is further configured to obtain ground information via an onboard sensor and render the ground point cloud map in real-time based on the ground information.
The system of any of claims 31-36, wherein the positioning device comprises a real time kinematic RTK mobile tip.
The system of any of claims 31-37, the airborne radar comprising a continuous wave radar or a lidar.
The system of claim 38, wherein the airborne radar is a continuous wave radar;

the processor is configured to determine, through an airborne radar of the drone, current relative altitude information of the drone with respect to the ground, and specifically includes:

acquiring first ranging data obtained by ranging the ground by a continuous wave radar in a rotating process, wherein the first ranging data comprises N data obtained by the continuous wave radar in a preset rotating angle interval, and N is an integer greater than 1;

clustering the first ranging data, and removing data with clustering density lower than preset density from the first ranging data to obtain second ranging data, wherein the second ranging data comprises M data, and M is a positive integer less than or equal to N;

and determining the terrain parameters of the ground according to the second ranging data, wherein the terrain parameters comprise the relative height information of the ground just below the continuous wave radar distance.
The system of claim 39, wherein each datum comprises:

the rotation angle of the continuous wave radar and the distance between the rotation angle and a ground ranging point;

or the horizontal distance and the vertical distance of the continuous wave radar from the ground ranging point.
The system according to claim 40, wherein the processor is configured to perform clustering on the first ranging data, and remove data with a clustering density lower than a preset density from the first ranging data to obtain second ranging data, and specifically includes:

performing coordinate conversion on the first ranging data to obtain coordinates corresponding to the N data;

mapping the coordinates of the N data to a predetermined first matrix;

mapping each coordinate to a position in the first matrix, and setting a matrix element of the position in the first matrix as a first preset element to obtain a second matrix;

clustering the second matrix according to a preset clustering sliding window, and removing matrix elements with clustering density lower than preset density from the second matrix to obtain M first preset elements;

obtaining the second ranging data from the first ranging data according to the M first preset elements;

wherein matrix elements in the first matrix are different from the first preset elements.
The system according to claim 41, wherein the processor is configured to obtain the second ranging data from the first ranging data according to the M first preset elements, and specifically includes:

determining M coordinates respectively corresponding to the M first preset elements according to the positions of the M first preset elements in the second matrix;

and according to the M coordinates, determining data respectively corresponding to the M coordinates from the first ranging data as the second ranging data.
The system according to claim 41 or 42, wherein the processor is configured to perform coordinate transformation on the first ranging data to obtain coordinates corresponding to the N data, and specifically includes:

establishing a rectangular coordinate system by taking the rotation center of the continuous wave radar as an origin, taking the direction right before the rotation of the continuous wave radar as the positive direction of an x axis and taking the vertical downward direction as the positive direction of a y axis;

and performing coordinate conversion on each data in the first ranging data according to the rectangular coordinate system to obtain a coordinate corresponding to each data.
The system according to claim 41, wherein the processor is configured to map the coordinates of the N data into a predetermined first matrix, and specifically comprises:

determining a column position of each data mapped to the first matrix according to a horizontal coordinate in the coordinates of each data, a maximum horizontal distance detected by the continuous wave radar, and a resolution of the continuous wave radar detection distance;

and determining the row position of each data mapped to the first matrix according to the vertical coordinate in the coordinate of each data, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.
The system according to claim 42, wherein the processor is configured to determine, according to the positions of the M first preset elements in the second matrix, M coordinates respectively corresponding to the M first preset elements, specifically including:

for each first preset element, determining a horizontal coordinate in coordinates corresponding to the first preset element according to the row position of the first preset element in the second matrix, the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining a vertical coordinate in the coordinate corresponding to the first preset element according to the column position of the first preset element in the second matrix, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.
The system of any one of claims 41-45, wherein the processor is further configured to:

determining the number of rows of the first matrix according to the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

determining the number of columns of the first matrix according to the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining the first matrix according to the row number and the column number.
The system of any of claims 41-46, wherein the first matrix is a null matrix.
The system of any one of claims 41-47,

the processor is configured to perform clustering operation on the second matrix according to a preset clustering sliding window, and remove matrix elements with clustering density lower than a preset density from the second matrix to obtain M first preset elements, and specifically includes:

performing sliding window operation on the second matrix according to the preset size of the cluster sliding window to obtain matrix elements corresponding to preset anchor points in each cluster sliding window;

and according to a cluster sliding window with the matrix element corresponding to the preset anchor point as a first preset element, eliminating the matrix element with the clustering density lower than the preset density from the second matrix to obtain the M first preset elements.
The system according to claim 48, wherein the processor is configured to, according to a cluster sliding window in which a matrix element corresponding to a preset anchor point is a first preset element, remove a matrix element with a cluster density lower than a preset density from the second matrix, and obtain the M first preset elements, specifically including:

for each cluster sliding window, if a matrix element corresponding to a preset anchor point in the cluster sliding window is a first preset element, acquiring the number of the first preset elements in the cluster sliding window;

if the number of the first preset elements is smaller than a first preset value, changing matrix elements corresponding to preset anchor points in the cluster sliding window into second preset elements, and if the number of the first preset elements is larger than or equal to the first preset value, keeping the matrix elements corresponding to the preset anchor points in the cluster sliding window as the first preset elements;

determining all the first preset elements in the second matrix after the change processing to be the M first preset elements.
The system according to claim 48 or 49, wherein the processor is configured to perform a sliding window operation on the second matrix according to a preset cluster sliding window size, and specifically includes:

determining a starting anchor point and a terminating anchor point of a preset cluster sliding window from the second matrix;

and starting from the initial anchor point, performing sliding window operation on the second matrix according to the preset size of the cluster sliding window, and stopping the sliding window operation until the termination anchor point is reached.
The system according to claim 50, wherein the start anchor point is a position of the second matrix where the first predetermined element has the smallest row number and the smallest column number, and the end anchor point is a position of the second matrix where the first predetermined element has the largest row number and the largest column number.
The system according to any one of claims 39 to 51, wherein the processor is configured to determine a topographical parameter of the ground surface based on the second ranging data, and specifically comprises:

and when the number M of the data included in the second ranging data is larger than or equal to a second preset value, determining the terrain parameters of the ground according to the second ranging data.
The system according to any one of claims 39 to 52, wherein the processor is configured to determine a topographical parameter of the ground surface based on the second ranging data, and specifically comprises:

performing linear fitting according to the second ranging data to obtain a linear function;

and determining the terrain parameters of the ground according to the straight line function.
The system of claim 53, wherein the processor is configured to determine a terrain parameter of the ground based on the linear function, and in particular comprises:

and determining the height value of the continuous wave radar from the ground right below according to the intercept in the linear function.
The system according to any one of claims 39 to 54, wherein the processor is configured to obtain first ranging data for ground ranging by the continuous wave radar during rotation, and specifically comprises:

acquiring third ranging data of the continuous wave radar for ranging the ground in the rotation process; the third ranging data comprise H data, the H data are all data for ranging the ground when the rotation angle of the continuous wave radar is within a preset angle interval, and H is an integer greater than or equal to N;

and acquiring the first ranging data according to the third ranging data.
The system according to claim 55, wherein the processor is configured to obtain the first ranging data according to the third ranging data, and specifically includes:

determining the first ranging data according to the third ranging data and an effective ranging condition;

wherein, the effective ranging condition includes: less than or equal to a preset maximum distance and greater than or equal to a preset minimum distance.
The system of claim 56, wherein the processor is configured to determine the first ranging data according to the third ranging data and a valid ranging range, and specifically comprises:

determining N data satisfying the effective ranging condition from the third ranging data;

and determining the first ranging data according to N data in the third ranging data.
The system according to claim 57, wherein the processor is configured to determine the first ranging data according to N data in the third ranging data, and specifically comprises:

determining N data in the third ranging data as the first ranging data; or,

and performing smoothing processing on the N data in the third ranging data to obtain the first ranging data.
The system of claim 58, wherein the processor configured to smooth N data of the third ranging data to obtain the first ranging data comprises:

sequencing N data in the third ranging data according to the sequence of the rotation angles of the continuous wave radar;

determining that the 1 st data in the sequenced third ranging data is the 1 st data in the first ranging data, and the Nth data in the sequenced third ranging data is the Nth data in the first ranging data;

determining the average value of the j-1 th data, the j-1 th data and the j +1 th data in the sequenced third ranging data as the j-th data in the first ranging data;

wherein j is an integer of 2 or more and N-1 or less.
The system of any one of claims 55 to 59, wherein the obtaining third ranging data for the continuous wave radar to range the ground during rotation comprises:

acquiring all data of ground ranging of a continuous wave radar rotating for one circle and a rotation angle of the continuous wave radar corresponding to each data;

and acquiring data corresponding to the rotation angle of the continuous wave radar within the preset angle interval as third ranging data according to the preset angle interval.
A device for evaluating the accuracy of a ground point cloud map is characterized by comprising: positioning equipment, an airborne radar and a processor;

the processor is used for determining the current position information and absolute height information of the unmanned aerial vehicle through the positioning equipment;

the processor is further used for determining the current relative height information of the unmanned aerial vehicle relative to the ground through the airborne radar;

the processor is further configured to obtain elevation data of a flight path of the unmanned aerial vehicle according to the position information, the absolute altitude information and the relative altitude information; and the altitude data of the flight path of the unmanned aerial vehicle is used for evaluating the precision of the ground point cloud map.
The apparatus according to claim 61, wherein the processor is further configured to:

and calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route.
The apparatus of claim 62, wherein the processor is configured to calibrate elevation data in the ground point cloud map based on elevation data for the flight path, and further comprising:

and taking the elevation data of the flight path as the elevation data after the calibration of the corresponding position of the flight path in the ground point cloud map.
The apparatus of claim 62, wherein the processor is configured to calibrate the elevation data in the ground point cloud map according to the elevation data of the flight path of the UAV, and specifically comprises:

determining the calibration quantity of the elevation data of the corresponding position in the ground point cloud map according to the elevation data of the flight route and the elevation data of the corresponding position of the flight route in the ground point cloud map;

and taking the elevation data of the corresponding position in the ground point cloud map and the operation result of the calibration amount as the elevation data after the corresponding position in the ground point cloud map is calibrated.
The apparatus of any one of claims 62-64, wherein the processor is further configured to determine an accuracy of the ground point cloud map based on elevation data for the flight path;

the processor is used for calibrating the elevation data in the ground point cloud map according to the elevation data of the flight path of the unmanned aerial vehicle, and specifically comprises the following steps:

and if the precision of the ground point cloud map is smaller than a precision threshold value, the unmanned aerial vehicle calibrates the elevation data in the ground point cloud map according to the elevation data of the flight path of the unmanned aerial vehicle.
The apparatus of any one of claims 61-65, wherein the processor is further configured to obtain ground information via an onboard sensor, and to map the cloud of ground points in real time based on the ground information.
The apparatus of any one of claims 61-63, wherein the positioning device comprises a real time kinematic RTK mobile terminal.
The apparatus of any one of claims 61-67, the airborne radar comprising a continuous wave radar or a lidar.
The apparatus of claim 68, wherein the airborne radar is a continuous wave radar;

the processor is configured to determine, through an airborne radar of the drone, current relative altitude information of the drone with respect to the ground, and specifically includes:

acquiring first ranging data obtained by ranging the ground by a continuous wave radar in a rotating process, wherein the first ranging data comprises N data obtained by the continuous wave radar in a preset rotating angle interval, and N is an integer greater than 1;

clustering the first ranging data, and removing data with clustering density lower than preset density from the first ranging data to obtain second ranging data, wherein the second ranging data comprises M data, and M is a positive integer less than or equal to N;

and determining the terrain parameters of the ground according to the second ranging data, wherein the terrain parameters comprise the relative height information of the ground just below the continuous wave radar distance.
The apparatus of claim 69, wherein each datum comprises:

the rotation angle of the continuous wave radar and the distance between the rotation angle and a ground ranging point;

or the horizontal distance and the vertical distance of the continuous wave radar from the ground ranging point.
The apparatus as claimed in claim 70, wherein the processor is configured to perform clustering on the first ranging data, and remove data with a clustering density lower than a preset density from the first ranging data to obtain second ranging data, and specifically includes:

performing coordinate conversion on the first ranging data to obtain coordinates corresponding to the N data;

mapping the coordinates of the N data to a predetermined first matrix;

mapping each coordinate to a position in the first matrix, and setting a matrix element of the position in the first matrix as a first preset element to obtain a second matrix;

clustering the second matrix according to a preset clustering sliding window, and removing matrix elements with clustering density lower than preset density from the second matrix to obtain M first preset elements;

obtaining the second ranging data from the first ranging data according to the M first preset elements;

wherein matrix elements in the first matrix are different from the first preset elements.
The apparatus as claimed in claim 71, wherein the processor is configured to obtain the second ranging data from the first ranging data according to the M first preset elements, and specifically includes:

determining M coordinates respectively corresponding to the M first preset elements according to the positions of the M first preset elements in the second matrix;

and according to the M coordinates, determining data respectively corresponding to the M coordinates from the first ranging data as the second ranging data.
The apparatus according to claim 71 or 72, wherein the processor is configured to perform coordinate transformation on the first ranging data to obtain coordinates corresponding to the N pieces of data, and specifically includes:

establishing a rectangular coordinate system by taking the rotation center of the continuous wave radar as an origin, taking the direction right before the rotation of the continuous wave radar as the positive direction of an x axis and taking the vertical downward direction as the positive direction of a y axis;

and performing coordinate conversion on each data in the first ranging data according to the rectangular coordinate system to obtain a coordinate corresponding to each data.
The apparatus of claim 71, wherein the processor is configured to map coordinates of the N data into a predetermined first matrix, and specifically comprises:

determining a column position of each data mapped to the first matrix according to a horizontal coordinate in the coordinates of each data, a maximum horizontal distance detected by the continuous wave radar, and a resolution of the continuous wave radar detection distance;

and determining the row position of each data mapped to the first matrix according to the vertical coordinate in the coordinate of each data, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.
The apparatus according to claim 72, wherein the processor is configured to determine, according to the positions of the M first preset elements in the second matrix, M coordinates respectively corresponding to the M first preset elements, specifically including:

for each first preset element, determining a horizontal coordinate in coordinates corresponding to the first preset element according to the row position of the first preset element in the second matrix, the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining a vertical coordinate in the coordinate corresponding to the first preset element according to the column position of the first preset element in the second matrix, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.
The apparatus according to any one of claims 71-75, wherein the processor is further configured to:

determining the number of rows of the first matrix according to the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

determining the number of columns of the first matrix according to the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining the first matrix according to the row number and the column number.
The apparatus of any of claims 71-76, wherein the first matrix is a null matrix.
The device of any one of claims 71-77,

the processor is configured to perform clustering operation on the second matrix according to a preset clustering sliding window, and remove matrix elements with clustering density lower than a preset density from the second matrix to obtain M first preset elements, and specifically includes:

performing sliding window operation on the second matrix according to the preset size of the cluster sliding window to obtain matrix elements corresponding to preset anchor points in each cluster sliding window;

and according to a cluster sliding window with the matrix element corresponding to the preset anchor point as a first preset element, eliminating the matrix element with the clustering density lower than the preset density from the second matrix to obtain the M first preset elements.
The apparatus of claim 78, wherein the processor is configured to, according to a cluster sliding window in which a matrix element corresponding to a preset anchor point is a first preset element, remove a matrix element with a cluster density lower than a preset density from the second matrix to obtain the M first preset elements, and specifically includes:

for each cluster sliding window, if a matrix element corresponding to a preset anchor point in the cluster sliding window is a first preset element, acquiring the number of the first preset elements in the cluster sliding window;

if the number of the first preset elements is smaller than a first preset value, changing matrix elements corresponding to preset anchor points in the cluster sliding window into second preset elements, and if the number of the first preset elements is larger than or equal to the first preset value, keeping the matrix elements corresponding to the preset anchor points in the cluster sliding window as the first preset elements;

determining all the first preset elements in the second matrix after the change processing to be the M first preset elements.
The apparatus of claim 78 or 79, wherein the processor is configured to perform a sliding window operation on the second matrix according to a preset cluster sliding window size, and specifically includes:

determining a starting anchor point and a terminating anchor point of a preset cluster sliding window from the second matrix;

and starting from the initial anchor point, performing sliding window operation on the second matrix according to the preset size of the cluster sliding window, and stopping the sliding window operation until the termination anchor point is reached.
The apparatus of claim 80, wherein the start anchor point is a position of the second matrix where a row number and a column number of a first predetermined element are the smallest, and wherein the end anchor point is a position of the second matrix where a row number and a column number of a first predetermined element are the largest.
The apparatus of any one of claims 69 to 81, wherein the processor is configured to determine a topographical parameter of the surface based on the second ranging data, and specifically comprises:

and when the number M of the data included in the second ranging data is larger than or equal to a second preset value, determining the terrain parameters of the ground according to the second ranging data.
The apparatus of any one of claims 69-82, wherein the processor is configured to determine a topographical parameter of the surface based on the second ranging data, and specifically comprises:

performing linear fitting according to the second ranging data to obtain a linear function;

and determining the terrain parameters of the ground according to the straight line function.
The apparatus according to claim 83, wherein the processor is configured to determine a topographical parameter of the surface based on the linear function, and specifically comprises:

and determining the height value of the continuous wave radar from the ground right below according to the intercept in the linear function.
The apparatus of any one of claims 69-84, wherein the processor is configured to obtain first ranging data for ground ranging by the continuous wave radar during rotation, and specifically comprises:

acquiring third ranging data of the continuous wave radar for ranging the ground in the rotation process; the third ranging data comprise H data, the H data are all data for ranging the ground when the rotation angle of the continuous wave radar is within a preset angle interval, and H is an integer greater than or equal to N;

and acquiring the first ranging data according to the third ranging data.
The apparatus of claim 85, wherein the processor is configured to obtain the first ranging data according to the third ranging data, and specifically comprises:

determining the first ranging data according to the third ranging data and an effective ranging condition;

wherein, the effective ranging condition includes: less than or equal to a preset maximum distance and greater than or equal to a preset minimum distance.
The apparatus of claim 86, wherein the processor is configured to determine the first ranging data according to the third ranging data and a valid ranging range, and specifically comprises:

determining N data satisfying the effective ranging condition from the third ranging data;

and determining the first ranging data according to N data in the third ranging data.
The apparatus of claim 87, wherein the processor is configured to determine the first ranging data according to N data in the third ranging data, and specifically comprises:

determining N data in the third ranging data as the first ranging data; or,

and performing smoothing processing on the N data in the third ranging data to obtain the first ranging data.
The apparatus of claim 88, wherein the processor configured to smooth N of the third ranging data to obtain the first ranging data comprises:

sequencing N data in the third ranging data according to the sequence of the rotation angles of the continuous wave radar;

determining that the 1 st data in the sequenced third ranging data is the 1 st data in the first ranging data, and the Nth data in the sequenced third ranging data is the Nth data in the first ranging data;

determining the average value of the j-1 th data, the j-1 th data and the j +1 th data in the sequenced third ranging data as the j-th data in the first ranging data;

wherein j is an integer of 2 or more and N-1 or less.
The apparatus of any one of claims 85 to 89, wherein the obtaining third ranging data for the continuous wave radar to range the ground during rotation comprises:

acquiring all data of ground ranging of a continuous wave radar rotating for one circle and a rotation angle of the continuous wave radar corresponding to each data;

and acquiring data corresponding to the rotation angle of the continuous wave radar within the preset angle interval as third ranging data according to the preset angle interval.
An unmanned aerial vehicle, comprising: positioning equipment, an airborne radar and a processor;

the processor is used for determining the current position information and the absolute height information of the unmanned aerial vehicle through the positioning equipment;

the processor is further used for determining the current relative height information of the unmanned aerial vehicle relative to the ground through the airborne radar;

the processor is further configured to obtain elevation data of a flight path of the unmanned aerial vehicle according to the position information, the absolute altitude information and the relative altitude information; and the altitude data of the flight path of the unmanned aerial vehicle is used for evaluating the precision of the ground point cloud map.
The drone of claim 91, wherein the processor is further configured to:

and calibrating the elevation data in the ground point cloud map according to the elevation data of the flight route.
The drone of claim 92, wherein the processor is configured to calibrate elevation data in the ground point cloud map based on elevation data for the flight path, including:

and taking the elevation data of the flight path as the elevation data after the calibration of the corresponding position of the flight path in the ground point cloud map.
The drone of claim 92, wherein the processor is configured to calibrate elevation data in the ground point cloud map according to elevation data of a flight path of the drone, including:

determining the calibration quantity of the elevation data of the corresponding position in the ground point cloud map according to the elevation data of the flight route and the elevation data of the corresponding position of the flight route in the ground point cloud map;

and taking the elevation data of the corresponding position in the ground point cloud map and the operation result of the calibration amount as the elevation data after the corresponding position in the ground point cloud map is calibrated.
A drone as in any of claims 92-94, wherein the processor is further configured to determine an accuracy of the ground point cloud map based on elevation data of the flight path;

the processor is used for calibrating the elevation data in the ground point cloud map according to the elevation data of the flight path of the unmanned aerial vehicle, and specifically comprises the following steps:

and if the precision of the ground point cloud map is smaller than a precision threshold value, the unmanned aerial vehicle calibrates the elevation data in the ground point cloud map according to the elevation data of the flight path of the unmanned aerial vehicle.
A drone as in any of claims 91-95, wherein the processor is further configured to obtain ground information via an onboard sensor and render the ground point cloud map in real-time based on the ground information.
A drone as claimed in any of claims 91-96, wherein the positioning device includes a real-time kinematic RTK mobile terminal.
The drone of any one of claims 91-97, the airborne radar comprising a continuous wave radar or a lidar.
A drone according to claim 98, wherein the airborne radar is a continuous wave radar;

the processor is configured to determine, through an airborne radar of the drone, current relative altitude information of the drone with respect to the ground, and specifically includes:

acquiring first ranging data obtained by ranging the ground by a continuous wave radar in a rotating process, wherein the first ranging data comprises N data obtained by the continuous wave radar in a preset rotating angle interval, and N is an integer greater than 1;

clustering the first ranging data, and removing data with clustering density lower than preset density from the first ranging data to obtain second ranging data, wherein the second ranging data comprises M data, and M is a positive integer less than or equal to N;

and determining the terrain parameters of the ground according to the second ranging data, wherein the terrain parameters comprise the relative height information of the ground just below the continuous wave radar distance.
A drone as claimed in claim 99, wherein each data includes:

the rotation angle of the continuous wave radar and the distance between the rotation angle and a ground ranging point;

or the horizontal distance and the vertical distance of the continuous wave radar from the ground ranging point.
The unmanned aerial vehicle of claim 100, wherein the processor is configured to perform clustering on the first ranging data, remove data with a clustering density lower than a preset density from the first ranging data, and obtain second ranging data, and specifically include:

performing coordinate conversion on the first ranging data to obtain coordinates corresponding to the N data;

mapping the coordinates of the N data to a predetermined first matrix;

mapping each coordinate to a position in the first matrix, and setting a matrix element of the position in the first matrix as a first preset element to obtain a second matrix;

clustering the second matrix according to a preset clustering sliding window, and removing matrix elements with clustering density lower than preset density from the second matrix to obtain M first preset elements;

obtaining the second ranging data from the first ranging data according to the M first preset elements;

wherein matrix elements in the first matrix are different from the first preset elements.
The drone of claim 101, wherein the processor is configured to obtain the second ranging data from the first ranging data according to the M first preset elements, and specifically includes:

determining M coordinates respectively corresponding to the M first preset elements according to the positions of the M first preset elements in the second matrix;

and according to the M coordinates, determining data respectively corresponding to the M coordinates from the first ranging data as the second ranging data.
An unmanned aerial vehicle according to claim 101 or 102, wherein the processor is configured to perform coordinate transformation on the first ranging data to obtain coordinates corresponding to the N data, and specifically includes:

establishing a rectangular coordinate system by taking the rotation center of the continuous wave radar as an origin, taking the direction right before the rotation of the continuous wave radar as the positive direction of an x axis and taking the vertical downward direction as the positive direction of a y axis;

and performing coordinate conversion on each data in the first ranging data according to the rectangular coordinate system to obtain a coordinate corresponding to each data.
The drone of claim 101, wherein the processor is configured to map the coordinates of the N data into a predetermined first matrix, and specifically includes:

determining a column position of each data mapped to the first matrix according to a horizontal coordinate in the coordinates of each data, a maximum horizontal distance detected by the continuous wave radar, and a resolution of the continuous wave radar detection distance;

and determining the row position of each data mapped to the first matrix according to the vertical coordinate in the coordinate of each data, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.
A drone according to claim 102, wherein the processor is configured to determine, according to the positions of the M first preset elements in the second matrix, M coordinates respectively corresponding to the M first preset elements, and specifically includes:

for each first preset element, determining a horizontal coordinate in coordinates corresponding to the first preset element according to the row position of the first preset element in the second matrix, the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining a vertical coordinate in the coordinate corresponding to the first preset element according to the column position of the first preset element in the second matrix, the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar.
The drone of any of claims 101-105, wherein the processor is further configured to:

determining the number of rows of the first matrix according to the maximum horizontal distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

determining the number of columns of the first matrix according to the maximum vertical distance detected by the continuous wave radar and the resolution of the detection distance of the continuous wave radar;

and determining the first matrix according to the row number and the column number.
The drone of any of claims 101-106, wherein the first matrix is a null matrix.
The unmanned aerial vehicle of any of claims 101-107, wherein,

the processor is configured to perform clustering operation on the second matrix according to a preset clustering sliding window, and remove matrix elements with clustering density lower than a preset density from the second matrix to obtain M first preset elements, and specifically includes:

performing sliding window operation on the second matrix according to the preset size of the cluster sliding window to obtain matrix elements corresponding to preset anchor points in each cluster sliding window;

and according to a cluster sliding window with the matrix element corresponding to the preset anchor point as a first preset element, eliminating the matrix element with the clustering density lower than the preset density from the second matrix to obtain the M first preset elements.
The unmanned aerial vehicle of claim 108, wherein the processor is configured to, according to a cluster sliding window in which a matrix element corresponding to a preset anchor point is a first preset element, remove a matrix element with a cluster density lower than a preset density from the second matrix, and obtain the M first preset elements, specifically including:

for each cluster sliding window, if a matrix element corresponding to a preset anchor point in the cluster sliding window is a first preset element, acquiring the number of the first preset elements in the cluster sliding window;

if the number of the first preset elements is smaller than a first preset value, changing matrix elements corresponding to preset anchor points in the cluster sliding window into second preset elements, and if the number of the first preset elements is larger than or equal to the first preset value, keeping the matrix elements corresponding to the preset anchor points in the cluster sliding window as the first preset elements;

determining all the first preset elements in the second matrix after the change processing to be the M first preset elements.
An unmanned aerial vehicle as claimed in claim 108 or 109, wherein the processor is configured to perform a sliding window operation on the second matrix according to a preset cluster sliding window size, and specifically includes:

determining a starting anchor point and a terminating anchor point of a preset cluster sliding window from the second matrix;

and starting from the initial anchor point, performing sliding window operation on the second matrix according to the preset size of the cluster sliding window, and stopping the sliding window operation until the termination anchor point is reached.
A drone according to claim 110, wherein the start anchor point is the position of the second matrix where the first predetermined element is located with the smallest row number and the smallest column number, and the end anchor point is the position of the second matrix where the first predetermined element is located with the largest row number and the largest column number.
A drone as claimed in any of claims 99-111, wherein the processor is configured to determine the terrain parameter of the ground from the second ranging data, including:

and when the number M of the data included in the second ranging data is larger than or equal to a second preset value, determining the terrain parameters of the ground according to the second ranging data.
A drone of any of claims 99-112, wherein the processor is configured to determine a terrain parameter of the ground based on the second ranging data, including:

performing linear fitting according to the second ranging data to obtain a linear function;

and determining the terrain parameters of the ground according to the straight line function.
A drone according to claim 113, wherein the processor is configured to determine a terrain parameter of the ground based on the straight-line function, including:

and determining the height value of the continuous wave radar from the ground right below according to the intercept in the linear function.
A drone of any of claims 99-114, wherein the processor, configured to obtain first ranging data for continuous wave radar ranging to the ground during rotation, specifically includes:

acquiring third ranging data of the continuous wave radar for ranging the ground in the rotation process; the third ranging data comprise H data, the H data are all data for ranging the ground when the rotation angle of the continuous wave radar is within a preset angle interval, and H is an integer greater than or equal to N;

and acquiring the first ranging data according to the third ranging data.
An unmanned aerial vehicle as claimed in claim 115, wherein the processor is configured to obtain the first ranging data according to the third ranging data, and specifically includes:

determining the first ranging data according to the third ranging data and an effective ranging condition;

wherein, the effective ranging condition includes: less than or equal to a preset maximum distance and greater than or equal to a preset minimum distance.
A drone of claim 116, wherein the processor is configured to determine the first ranging data according to the third ranging data and the valid ranging range, and specifically includes:

determining N data satisfying the effective ranging condition from the third ranging data;

and determining the first ranging data according to N data in the third ranging data.
A drone according to claim 117, wherein the processor is configured to determine the first ranging data according to N of the third ranging data, and specifically includes:

determining N data in the third ranging data as the first ranging data; or,

and performing smoothing processing on the N data in the third ranging data to obtain the first ranging data.
A drone of claim 118, wherein the processor, configured to smooth N of the third ranging data to obtain the first ranging data, comprises:

sequencing N data in the third ranging data according to the sequence of the rotation angles of the continuous wave radar;

determining that the 1 st data in the sequenced third ranging data is the 1 st data in the first ranging data, and the Nth data in the sequenced third ranging data is the Nth data in the first ranging data;

determining the average value of the j-1 th data, the j-1 th data and the j +1 th data in the sequenced third ranging data as the j-th data in the first ranging data;

wherein j is an integer of 2 or more and N-1 or less.
The drone of any one of claims 115-119, wherein the obtaining of the third ranging data for the continuous wave radar to range to the ground during rotation comprises:

acquiring all data of ground ranging of a continuous wave radar rotating for one circle and a rotation angle of the continuous wave radar corresponding to each data;

and acquiring data corresponding to the rotation angle of the continuous wave radar within the preset angle interval as third ranging data according to the preset angle interval.
A computer-readable storage medium storing a computer program comprising at least one code section executable by a computer to control the computer to perform the point cloud map accuracy assessment method of any one of claims 1-30.
A computer program for implementing the point cloud map accuracy assessment method according to any one of claims 1 to 30 when the computer program is executed by a computer.