CN113362468A - Dimension measuring method for hub of train wheel - Google Patents
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Abstract
The invention relates to a dimension measuring method of a hub of a train wheel. The method comprises the following steps: acquiring a three-dimensional point cloud of a train hub, and performing down-sampling processing on the three-dimensional point cloud of the train hub to determine the down-sampling three-dimensional point cloud; filtering outliers in the down-sampling three-dimensional point cloud, and determining the filtered three-dimensional point cloud of the hub of the train; the outliers comprise noise point clouds and artifacts; rotating the filtered three-dimensional point cloud of the train wheel hub at different angles to measure for multiple times, and determining coordinates of the three-dimensional point cloud of the train wheel hub at different positions; and processing the coordinates of the three-dimensional point cloud of the hub of the train wheel at different positions by utilizing a multi-scale fusion Gaussian weight distribution algorithm to determine the size of the position to be measured. The invention can accurately and reliably measure the dimensions of the hub of the train at different positions and angles.
Description
Technical Field
The invention relates to the technical field of three-dimensional point cloud processing technology and computer science, in particular to a dimension measuring method of a train hub.
Background
The measurement of products and parts in industry is often performed by manual methods which are time consuming, inefficient and laborious. The existing common measuring instrument capable of being operated manually can only measure the size of a small object and cannot measure a large-size object. In addition to this, in the case of manual measurement, the measurement accuracy sometimes depends on the operational skill of the worker, such as: when the inner diameter dial indicator is used for measuring the inner diameter, if the inner diameter dial indicator is not accurately placed, an accurate value cannot be necessarily measured. In addition to the above disadvantages, manual measurement is inefficient, and the scale values obtained from some measuring tools (such as vernier caliper, radius micrometer) need further calculation to obtain accurate measurement values. For large sized objects such as train wheels, the manual method is more time consuming, inefficient and laborious.
In order to solve the above problems, a series of automated dimension measuring instruments have been developed. However, when these instruments measure dimensions, the object still needs to be placed on the platform, which results in that the range of the measured dimensions is still not large, and manual assistance is still needed, and the measurement efficiency is low. In addition to the above disadvantages, these measuring instruments are expensive and relatively low cost-effective. Due to the particularity of the train hub, the measurement efficiency of manual auxiliary operation is lower, and the sizes of the train hub at different positions and angles cannot be accurately and reliably measured.
Disclosure of Invention
The invention aims to provide a dimension measuring method of a train hub, which aims to solve the problems that the measuring efficiency is low and the dimensions of the train hub at different positions and angles cannot be accurately and reliably measured.
In order to achieve the purpose, the invention provides the following scheme:
a dimension measuring method of a railway wheel hub, comprising:
acquiring a three-dimensional point cloud of a train hub, and performing down-sampling processing on the three-dimensional point cloud of the train hub to determine the down-sampling three-dimensional point cloud;
filtering outliers in the down-sampling three-dimensional point cloud, and determining the filtered three-dimensional point cloud of the hub of the train; the outliers comprise noise point clouds and artifacts;
rotating the filtered three-dimensional point cloud of the train wheel hub at different angles to measure for multiple times, and determining coordinates of the three-dimensional point cloud of the train wheel hub at different positions;
and processing the coordinates of the three-dimensional point cloud of the hub of the train wheel at different positions by utilizing a multi-scale fusion Gaussian weight distribution algorithm to determine the size of the position to be measured.
Optionally, the obtaining of the three-dimensional point cloud of the hub of the train, and down-sampling the three-dimensional point cloud of the hub of the train to determine the down-sampled three-dimensional point cloud specifically include:
dividing the space of the three-dimensional point cloud of the hub of the train into a plurality of cubic spaces; each cubic space comprises a plurality of three-dimensional points of the train hubs;
and calculating the average value of all the three-dimensional points of the hub of the train in the cubic space, and taking the average value of the three-dimensional points of the hub of the train as down-sampling three-dimensional point cloud.
Optionally, the filtering is performed on outliers in the down-sampling three-dimensional point cloud, and the filtered three-dimensional point cloud of the hub of the train is determined, which specifically includes:
acquiring a plurality of field points of each down-sampling three-dimensional point in the down-sampling three-dimensional point cloud, and calculating the average distance value between the plurality of field points and the down-sampling three-dimensional point;
determining a mean value and a standard deviation according to the average distance values of all the down-sampling three-dimensional points;
and filtering outliers in the down-sampling three-dimensional point cloud according to the mean value and the standard deviation, and determining the filtered train wheel hub three-dimensional point cloud.
Optionally, the filtered three-dimensional point cloud of the hub of the train is rotated at different angles to perform multiple measurements, and coordinates of the three-dimensional point cloud of the hub of the train at different positions are determined, which specifically includes:
adjusting a three-dimensional coordinate system where the filtered three-dimensional point cloud of the hub of the train is located until an X axis is coincident with the axis of the hub of the train;
and taking 1 degree as a rotation angle, and performing multiple rotations and measurement on the filtered three-dimensional point cloud of the hub of the train around the X axis of the three-dimensional point cloud of the hub of the train by using a two-dimensional rotation formula to determine coordinates of the three-dimensional point cloud of the hub of the train at different positions.
Optionally, the processing, by using a multi-scale fusion gaussian weight distribution algorithm, the coordinates of the train hub three-dimensional point cloud at the different positions to determine the size of the position to be measured specifically includes:
dividing the position to be measured into 5 large areas;
rotating the train wheel hub three-dimensional point cloud to different large areas, and dividing the train wheel hub three-dimensional point cloud of the large area into 5 small areas;
calculating the coordinate average value of the coordinates of the three-dimensional point cloud of the hub of the train in each small area, and storing the coordinate average value of 5 small areas into a one-dimensional list with the length of 5;
carrying out weighted calculation on the coordinate average value of the 5 small areas according to the fixed Gaussian weight, and determining the sizes of different large area positions;
and determining the size of the position to be measured according to the sizes of the different large-area positions.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a dimension measuring method of a train hub, which directly carries out high-efficiency multi-angle, multi-position, accurate and robust measurement on three-dimensional point cloud data of the train hub without heavy and low-efficiency manual operation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a method for measuring dimensions of a hub of a railway vehicle according to the present invention;
FIG. 2 is a schematic diagram of a multiplication calculation of a localization value by a one-dimensional Gaussian weight kernel at two scales;
FIG. 3 is a schematic diagram of a measurement location and its neighboring small regions;
FIG. 4 is a schematic diagram of size value calculation;
FIG. 5 is a schematic view of the point cloud adjusted to have its axis coincident with the x-axis;
fig. 6 is a schematic view of a dimension measuring method of a hub of a train according to the present invention in an actual operation process.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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 invention aims to provide a dimension measuring method of a train hub, which can accurately and reliably measure dimensions of the train hub at different positions and angles.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a dimension measuring method for a hub of a train according to the present invention, and as shown in fig. 1, the dimension measuring method for a hub of a train includes:
step 101: acquiring a three-dimensional point cloud of a train hub, and performing down-sampling processing on the three-dimensional point cloud of the train hub to determine the down-sampling three-dimensional point cloud.
The step 101 specifically includes: dividing the space of the three-dimensional point cloud of the hub of the train into a plurality of cubic spaces; each cubic space comprises a plurality of three-dimensional points of the train hubs; and calculating the average value of all the three-dimensional points of the hub of the train in the cubic space, and taking the average value of the three-dimensional points of the hub of the train as down-sampling three-dimensional point cloud.
In practical application, firstly, the space of the three-dimensional point cloud of the hub of the train obtained by the matrix camera is cut into small cubic spaces (voxels) with fixed size v x v, then the three-dimensional points of the hub of the train are divided into each voxel, the coordinate average value of all the three-dimensional points of the hub of the train in each voxel is calculated, and the average value is used as the coordinate of the voxel, namely the down-sampling three-dimensional point cloud, so that the purpose of down-sampling is achieved.
Step 102: filtering outliers in the down-sampling three-dimensional point cloud, and determining the filtered three-dimensional point cloud of the hub of the train; the outliers include noise point clouds and artifacts.
The step 102 specifically includes: acquiring a plurality of field points of each down-sampling three-dimensional point in the down-sampling three-dimensional point cloud, and calculating the average distance value between the plurality of field points and the down-sampling three-dimensional point; determining a mean value and a standard deviation according to the average distance values of all the down-sampling three-dimensional points; and filtering outliers in the down-sampling three-dimensional point cloud according to the mean value and the standard deviation, and determining the filtered train wheel hub three-dimensional point cloud.
In practical applications, there are still many "noise" point clouds far from the train hub point cloud and some artifacts in the down-sampled three-dimensional point cloud. The statistical properties of the mean value and the variance are utilized to remove noise and artifacts far away from a point cloud main body, so that only main body point cloud (train wheel hub point cloud) is left in the whole three-dimensional space, and the purpose of filtering outliers is achieved. The formula is as follows:
assuming that n three-dimensional points are totally arranged in the down-sampling three-dimensional point cloud, i is any three-dimensional point, and stipulating each point P in the down-sampling three-dimensional point cloudiHas N neighborhood points, and calculates the distance P of the N pointsiAverage distance value S ofi：
Wherein, WiIs a point PiIs the point coordinates of the neighborhood points, and D (x, y, z) is the distance P between each neighborhood pointiThe distance of (c).
Each point PiAll have an S belonging to themi(average distances of N neighborhood points of the point from the point), these average distances SiObeying a gaussian distribution. The gaussian distribution now has a mean and a standard deviation. The mean and standard deviation are both calculated from the global mean:
calculating an average value:
the standard deviation σ was found:
setting a threshold value T, and judging whether a point Q is filtered out:
wherein: sQIs the average distance of the neighborhood point of the point Q from the point Q if mu-sigma T < S is satisfiedQIf the value is less than mu + sigma T, the point is reserved, if the value is not satisfied, the point is taken as an outlier, and filtering is carried out.
Step 103: and rotating the filtered three-dimensional point cloud of the hub of the train at different angles to measure for multiple times, and determining the coordinates of the three-dimensional point cloud of the hub of the train at different positions.
The step 103 specifically includes: adjusting a three-dimensional coordinate system where the filtered three-dimensional point cloud of the hub of the train is located until an X axis is coincident with the axis of the hub of the train; and taking 1 degree as a rotation angle, and performing multiple rotations and measurement on the filtered three-dimensional point cloud of the hub of the train around the X axis of the three-dimensional point cloud of the hub of the train by using a two-dimensional rotation formula to determine coordinates of the three-dimensional point cloud of the hub of the train at different positions.
In order to accurately measure the hub of the train wheel and improve the robustness of a measurement result, a point cloud rotation algorithm is used, and the point cloud is adjusted and rotated to measure the hub for multiple times (measuring the result every 1 degree). The point cloud is adjusted to a position to be rotated (the dimension of the hub of the train in the axial direction is measured, the three-dimensional coordinate system is adjusted to the position that the x axis of the three-dimensional coordinate system is coincident with the axis of the hub of the train) (fig. 5), and then the point cloud can be rotated around the x axis of the point cloud by using a two-dimensional rotation formula (fig. 5), wherein the formula is as follows:
where x is constant, (x, y ', z') is the new coordinates of each point in the rotated point cloud, i.e., the coordinates of the train hub three-dimensional point cloud at different locations.
Step 104: and processing the coordinates of the three-dimensional point cloud of the hub of the train wheel at different positions by utilizing a multi-scale fusion Gaussian weight distribution algorithm to determine the size of the position to be measured.
The step 104 specifically includes: dividing the position to be measured into 5 large areas; rotating the train wheel hub three-dimensional point cloud to different large areas, and dividing the train wheel hub three-dimensional point cloud of the large area into 5 small areas; calculating the coordinate average value of the coordinates of the three-dimensional point cloud of the hub of the train in each small area, and storing the coordinate average value of 5 small areas into a one-dimensional list with the length of 5; carrying out weighted calculation on the coordinate average value of the 5 small areas according to the fixed Gaussian weight, and determining the sizes of different large area positions; and determining the size of the position to be measured according to the sizes of the different large-area positions.
In practical application, the point cloud is rotated according to step 103, the position of the point cloud to be measured is transferred to the measurement position, and the point cloud obtains new coordinates (x, y ', z'). For example, to measure the accurate positioning value of the middle small area in fig. 2, the small area of the point cloud needs to be moved to the measurement position, as shown in fig. 3. At this time, the three-dimensional point obtains a new coordinate (x, y ', z'), divides the three-dimensional point in the middle small region into 5 small regions, and calculates an average value for each small region
And storing the five average values into a one-dimensional list with the length of 5, and performing weighted calculation on the five average values according to a fixed Gaussian weight (the middle weight is high, the weights on the two sides are low), wherein the calculation result is the accurate positioning value of the small area, such as the scale one shown in fig. 2.
The method is similar to an averaging method, which takes the average coordinates of all three-dimensional points in the small area as the positioning value of the small area.
The formula is as follows:
assuming that the small region has m points in total, it is divided into a one-dimensional list of length 5:
I＝{m1，m2，m3，m4，m5} (6)
wherein: m is1,m2,m3,m4,m5The points are divided into these 5 intervals, respectively.
Averaging the points in the 5 intervals to obtain a list I2：
I2＝{A1，A2，A3，A4，A5} (7)
Wherein: a. the1,A2,A3,A4,A5Point mean coordinates of 5 intervals each
Defining a Gaussian kernel G:
G＝{g1，g2，g3，g4，g5} (8)
wherein: g1,g2,g3,g4,g5Approximately Gaussian distribution, g3Maximum value, gradual decrease on both sides, g1、g2And minimum.
Calculating the accurate positioning value L of the small area, such as the scale one in FIG. 2:
wherein:
representing multiplication and addition of corresponding elements of the list, namely:
overlapping parts exist between the middle small area and the left and right adjacent small areas, the point cloud is rotated, the adjacent small areas are rotated to the measuring positions shown in the figure 3, and the accurate positioning values of the 4 adjacent small areas are measured according to the method. And then, putting the accurate positioning values of the 5 small regions into a one-dimensional list with the length of 5, performing weighted calculation according to a fixed Gaussian weight in a scale II shown in fig. 2, wherein the calculation result is the accurate and robust positioning value of the small region 1 in fig. 3, and in practical application, the Gaussian weight of the small region 1 is high, and the Gaussian weights of the small regions adjacent to two sides are low in fig. 3. The formula is as follows:
the accurate positioning values of the 5 small areas are put into a list I3The method comprises the following steps:
I3＝{L1，L2，L3，L4，L5} (11)
defining a Gaussian kernel G:
G＝{g1，g2，g3，g4，g5} (12)
wherein, g1,g2,g3,g4,g5Approximately Gaussian distribution, g3Maximum value, gradual decrease on both sides, g1、g2And minimum.
Calculating a small-region accurate and robust positioning value L', namely a final positioning value, namely a second dimension in the graph 2:
wherein,
representing multiplication and addition of corresponding elements of the list, namely:
the obtained accurate and robust positioning value L 'at the designated position of the point cloud is related to the coordinates of the three-dimensional point in the small region at the position, namely, the scale I in FIG. 2, so as to obtain the accurate positioning value L, and is also related to the positioning value of the small region at the adjacent position, namely, the scale II in FIG. 2, so as to obtain the final accurate and robust positioning value L' based on L.
The precise and robust positioning value of the small region 2 in fig. 3 is calculated in the same manner as above; after the two small-area accurate and robust positioning values are obtained, the accurate and robust size value can be obtained by calculating (subtracting) the positioning values of the two small-area accurate and robust positioning values. For example, as shown in fig. 4, after obtaining the positioning values of small region 1 and small region 2, the accurate and robust size values in the x direction and the z direction can be obtained by subtracting, and the formula is as follows:
wherein, L'x1，L′y1Is an accurate and robust positioning value, L ', of small region 1'x2，L′y2Is an accurate and robust localization value for small region 2. L'xAnd L'yThe x-direction and y-direction precise and robust positioning differences, i.e. dimension values, respectively. And after an accurate and robust positioning difference value (size value) is obtained, wear judgment can be carried out according to the standard size value.
Fig. 6 is a schematic view of the dimension measuring method for a hub of a train according to the present invention in an actual operation process, as shown in fig. 6, a matrix camera is used to obtain point cloud data (x, y, z) of the hub of the train; converting the spatial midpoint coordinate into a down-sampling model of the voxel coordinate by using a voxel down-sampling algorithm; constructing an outlier filtering model for removing 'noise' points in the space by using a statistical outlier removing algorithm; performing multi-angle rotation on the point cloud by using the proposed point cloud rotation algorithm to measure accurate and robust size values at different positions; and calculating an accurate and robust positioning value at the appointed position of the point cloud by using a proposed multi-scale Gaussian weight fusion algorithm, and subtracting the two positioning values to obtain an accurate and robust size value.
The measurement of the hub of the train does not need heavy and low-efficiency manual operation, and the three-dimensional point cloud data of the hub of the train can be directly measured in a high-efficiency, multi-position, accurate and robust mode. The method is applied to the measurement of the size of the train wheel hub and the judgment of the abrasion, can accurately and reliably measure the sizes of the train wheel hubs at different positions and angles to judge the abrasion condition, improves the efficiency and the accuracy of the measurement of the size of the train wheel and the judgment of the abrasion to a great extent, greatly improves the accuracy while greatly reducing the labor cost, and has strong practical value in industry.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (5)
1. A method of measuring dimensions of a hub of a railway wheel, comprising:
acquiring a three-dimensional point cloud of a train hub, and performing down-sampling processing on the three-dimensional point cloud of the train hub to determine the down-sampling three-dimensional point cloud;
filtering outliers in the down-sampling three-dimensional point cloud, and determining the filtered three-dimensional point cloud of the hub of the train; the outliers comprise noise point clouds and artifacts;
rotating the filtered three-dimensional point cloud of the train wheel hub at different angles to measure for multiple times, and determining coordinates of the three-dimensional point cloud of the train wheel hub at different positions;
and processing the coordinates of the three-dimensional point cloud of the hub of the train wheel at different positions by utilizing a multi-scale fusion Gaussian weight distribution algorithm to determine the size of the position to be measured.
2. The method for measuring the size of the hub of the train as claimed in claim 1, wherein the step of obtaining the three-dimensional point cloud of the hub of the train and performing down-sampling processing on the three-dimensional point cloud of the hub of the train to determine the down-sampled three-dimensional point cloud specifically comprises the following steps:
dividing the space of the three-dimensional point cloud of the hub of the train into a plurality of cubic spaces; each cubic space comprises a plurality of three-dimensional points of the train hubs;
and calculating the average value of all the three-dimensional points of the hub of the train in the cubic space, and taking the average value of the three-dimensional points of the hub of the train as down-sampling three-dimensional point cloud.
3. The method for measuring the dimensions of a train hub according to claim 1, wherein the filtering out outliers in the down-sampled three-dimensional point cloud and determining the filtered out train hub three-dimensional point cloud specifically comprises:
acquiring a plurality of field points of each down-sampling three-dimensional point in the down-sampling three-dimensional point cloud, and calculating the average distance value between the plurality of field points and the down-sampling three-dimensional point;
determining a mean value and a standard deviation according to the average distance values of all the down-sampling three-dimensional points;
and filtering outliers in the down-sampling three-dimensional point cloud according to the mean value and the standard deviation, and determining the filtered train wheel hub three-dimensional point cloud.
4. The method for measuring the size of the hub of the train as claimed in claim 1, wherein the rotating the filtered three-dimensional point cloud of the hub of the train at different angles for a plurality of times to determine the coordinates of the three-dimensional point cloud of the hub of the train at different positions specifically comprises:
adjusting a three-dimensional coordinate system where the filtered three-dimensional point cloud of the hub of the train is located until an X axis is coincident with the axis of the hub of the train;
and taking 1 degree as a rotation angle, and performing multiple rotations and measurement on the filtered three-dimensional point cloud of the hub of the train around the X axis of the three-dimensional point cloud of the hub of the train by using a two-dimensional rotation formula to determine coordinates of the three-dimensional point cloud of the hub of the train at different positions.
5. The method for measuring the dimensions of the hub of the train as claimed in claim 1, wherein the determining the dimensions of the positions to be measured by processing the coordinates of the three-dimensional point cloud of the hub of the train at the different positions by using a multi-scale fusion gaussian weight distribution algorithm specifically comprises:
dividing the position to be measured into 5 large areas;
rotating the train wheel hub three-dimensional point cloud to different large areas, and dividing the train wheel hub three-dimensional point cloud of the large area into 5 small areas;
calculating the coordinate average value of the coordinates of the three-dimensional point cloud of the hub of the train in each small area, and storing the coordinate average value of 5 small areas into a one-dimensional list with the length of 5;
carrying out weighted calculation on the coordinate average value of the 5 small areas according to the fixed Gaussian weight, and determining the sizes of different large area positions;
and determining the size of the position to be measured according to the sizes of the different large-area positions.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113793384A (en) * | 2021-09-15 | 2021-12-14 | 上海大学 | Petroleum drill pipe diameter-changing positioning method and system based on image processing |
| CN114264491A (en) * | 2021-12-27 | 2022-04-01 | 中车青岛四方车辆研究所有限公司 | Rail vehicle wheel pair parameter detection system |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101256156A (en) * | 2008-04-09 | 2008-09-03 | 西安电子科技大学 | Precision measurement method for flat crack and antenna crack |
| CN101566461A (en) * | 2009-05-18 | 2009-10-28 | 西安交通大学 | Method for quickly measuring blade of large-sized water turbine |
| WO2010108099A1 (en) * | 2009-03-19 | 2010-09-23 | William Marsh Rice University | Method and apparatus for compressive domain filtering and interference cancelation |
| CN101914881A (en) * | 2010-07-27 | 2010-12-15 | 唐粮 | Method for rapidly measuring foundation pile control net (CPIII) of rapid transit railway |
| CN102003938A (en) * | 2010-10-11 | 2011-04-06 | 中国人民解放军信息工程大学 | Thermal state on-site detection method for large high-temperature forging |
| CN102126162A (en) * | 2010-12-26 | 2011-07-20 | 北京航空航天大学 | Numerical control machine processing online measurement method |
| CN102750547A (en) * | 2012-06-11 | 2012-10-24 | 陕西科技大学 | Fruit size grading method based on compressed sensing |
| CN103047943A (en) * | 2012-12-31 | 2013-04-17 | 吉林大学 | Method for detecting vehicle door outer plate shape and size based on single-projection encoding structured light |
| CN103335626A (en) * | 2013-06-04 | 2013-10-02 | 上海交通大学 | An optimization selecting method for flatness measuring by a three-coordinate measuring machine |
| CN105091748A (en) * | 2015-05-29 | 2015-11-25 | 南京南车浦镇城轨车辆有限责任公司 | Measuring system for tolerance dimensions of railway vehicle |
| WO2017124654A1 (en) * | 2016-01-22 | 2017-07-27 | 尹兴 | Three-dimensional measurement system and measurement method for feature point based on plane of four-camera set array |
| CN108459319A (en) * | 2018-03-13 | 2018-08-28 | 燕山大学 | A kind of quick scanning system of vehicle running region Terrain Elevation |
| CN109141364A (en) * | 2018-08-01 | 2019-01-04 | 北京进化者机器人科技有限公司 | Obstacle detection method, system and robot |
| CN109238168A (en) * | 2018-08-06 | 2019-01-18 | 大连理工大学 | Large-scale metrology part surface three dimension shape high-precision measuring method |
| CN110230978A (en) * | 2018-03-14 | 2019-09-13 | 浙江大学山东工业技术研究院 | A kind of refractory brick geometric dimension measurement method |
| CN111696210A (en) * | 2020-04-22 | 2020-09-22 | 北京航天控制仪器研究所 | Point cloud reconstruction method and system based on three-dimensional point cloud data characteristic lightweight |
| CN112651965A (en) * | 2021-01-14 | 2021-04-13 | 成都铁安科技有限责任公司 | Three-dimensional detection method and system for wheel tread defects |
-
2021
- 2021-07-05 CN CN202110756492.7A patent/CN113362468B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101256156A (en) * | 2008-04-09 | 2008-09-03 | 西安电子科技大学 | Precision measurement method for flat crack and antenna crack |
| WO2010108099A1 (en) * | 2009-03-19 | 2010-09-23 | William Marsh Rice University | Method and apparatus for compressive domain filtering and interference cancelation |
| CN101566461A (en) * | 2009-05-18 | 2009-10-28 | 西安交通大学 | Method for quickly measuring blade of large-sized water turbine |
| CN101914881A (en) * | 2010-07-27 | 2010-12-15 | 唐粮 | Method for rapidly measuring foundation pile control net (CPIII) of rapid transit railway |
| CN102003938A (en) * | 2010-10-11 | 2011-04-06 | 中国人民解放军信息工程大学 | Thermal state on-site detection method for large high-temperature forging |
| CN102126162A (en) * | 2010-12-26 | 2011-07-20 | 北京航空航天大学 | Numerical control machine processing online measurement method |
| CN102750547A (en) * | 2012-06-11 | 2012-10-24 | 陕西科技大学 | Fruit size grading method based on compressed sensing |
| CN103047943A (en) * | 2012-12-31 | 2013-04-17 | 吉林大学 | Method for detecting vehicle door outer plate shape and size based on single-projection encoding structured light |
| CN103335626A (en) * | 2013-06-04 | 2013-10-02 | 上海交通大学 | An optimization selecting method for flatness measuring by a three-coordinate measuring machine |
| CN105091748A (en) * | 2015-05-29 | 2015-11-25 | 南京南车浦镇城轨车辆有限责任公司 | Measuring system for tolerance dimensions of railway vehicle |
| WO2017124654A1 (en) * | 2016-01-22 | 2017-07-27 | 尹兴 | Three-dimensional measurement system and measurement method for feature point based on plane of four-camera set array |
| CN108459319A (en) * | 2018-03-13 | 2018-08-28 | 燕山大学 | A kind of quick scanning system of vehicle running region Terrain Elevation |
| CN110230978A (en) * | 2018-03-14 | 2019-09-13 | 浙江大学山东工业技术研究院 | A kind of refractory brick geometric dimension measurement method |
| CN109141364A (en) * | 2018-08-01 | 2019-01-04 | 北京进化者机器人科技有限公司 | Obstacle detection method, system and robot |
| CN109238168A (en) * | 2018-08-06 | 2019-01-18 | 大连理工大学 | Large-scale metrology part surface three dimension shape high-precision measuring method |
| CN111696210A (en) * | 2020-04-22 | 2020-09-22 | 北京航天控制仪器研究所 | Point cloud reconstruction method and system based on three-dimensional point cloud data characteristic lightweight |
| CN112651965A (en) * | 2021-01-14 | 2021-04-13 | 成都铁安科技有限责任公司 | Three-dimensional detection method and system for wheel tread defects |
Non-Patent Citations (6)
| Title |
|---|
| LÁSZLÓ KUDELA 等: "Direct structural analysis of domains defined by point clouds", 《COMPUT. METHODS APPL. MECH. ENGRG》 * |
| LÁSZLÓ KUDELA 等: "Direct structural analysis of domains defined by point clouds", 《COMPUT. METHODS APPL. MECH. ENGRG》, 31 December 2020 (2020-12-31), pages 1 - 21 * |
| MIAO GONG 等: "Three-Dimensional Measurement Method of Four-View Stereo Vision Based on Gaussian Process Regression", 《SENSORS》 * |
| MIAO GONG 等: "Three-Dimensional Measurement Method of Four-View Stereo Vision Based on Gaussian Process Regression", 《SENSORS》, 31 December 2019 (2019-12-31), pages 1 - 15 * |
| 许梦兵 等: "基于激光点云的高精度井盖提取和沉降病害检测方法研究", 《中国激光》 * |
| 许梦兵 等: "基于激光点云的高精度井盖提取和沉降病害检测方法研究", 《中国激光》, 29 September 2020 (2020-09-29), pages 1 - 20 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113793384A (en) * | 2021-09-15 | 2021-12-14 | 上海大学 | Petroleum drill pipe diameter-changing positioning method and system based on image processing |
| CN113793384B (en) * | 2021-09-15 | 2023-08-22 | 上海大学 | Image processing-based oil drill pipe reducing positioning method and system |
| CN114264491A (en) * | 2021-12-27 | 2022-04-01 | 中车青岛四方车辆研究所有限公司 | Rail vehicle wheel pair parameter detection system |
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