KR102252295B1 - Method and autonomous mobile robot for generating indoor topology map - Google Patents

Abstract

Disclosed are a method for generating an indoor topology map and an autonomous mobile robot therefor. The method for generating an indoor topology map includes the following steps of: starting driving indoors; acquiring surrounding shape data using a LiDAR sensor; performing a loop closure inspection on a preset driving route; setting a planned driving route so that the preset driving route and the planned driving route do not intersect according to the loop closure inspection result; generating an indoor map using the acquired surrounding shape data when the driving is finished; and generating an indoor topology map using the generated indoor map.

Description

Method and autonomous mobile robot for generating indoor topology map

The present invention relates to a method for generating an indoor topology map and an autonomous driving robot therefor.

In general, the map data is generated so that POI (Point of Interest) around the road and traffic guide signs can be displayed on a screen centering on a road. Navigation using such map data can output related information on the screen along with the current location information of the vehicle when the vehicle travels on the road. However, when entering an interior such as a building structure or an underground space, GPS (Global Positioning System) There is a problem in that the current position information cannot be obtained because it is difficult to receive signals.

In order to compensate for this problem, conventionally, a two-dimensional or three-dimensional navigation screen based on indoor map data constructed using CAD drawings of a building or the like has been provided to a user. However, in order to construct the indoor map data in this manner, it is difficult to create an indoor map for a building that does not have a CAD drawing, since it is necessary to use data such as CAD drawings related to the design of the building. Moreover, since the CAD drawing contains details of the structure of the building, there is a limitation that the building owner does not easily disclose it to the outside, so it was practically impossible to use the CAD drawing of the building in order to create an indoor map in reality.

Accordingly, in recent years, a technology in which a person directly scans an indoor room using a device equipped with a LiDAR (Light Detection and Ranging) to create an indoor map has been used.

LiDAR (Light Detection and Ranging) is a sensor that measures distance and detects objects using light. It was first developed for meteorological observation in the 1930s, but in the 1960s, when laser technology appeared, it began to be used in earnest. At that time, it was mainly applied to the aviation field and satellites, but after that, it was applied to the global environment, exploration, automobiles, and robots. The radar has a principle similar to that of radar. The radar emits electromagnetic waves to the outside and checks the distance and direction with the re-received electromagnetic waves. On the other hand, the difference is that Rida uses a pulsed laser. By using a laser with a short wavelength, Rida has a feature that increases precision and resolution, and enables three-dimensional grasp of objects depending on the object.

Although various technologies for creating indoor or outdoor maps using such a lidar have been developed and used, in practice, environmental data is collected and a map is created by manipulating a device equipped with a lidar. There is a problem in that the quality of the created map may be deteriorated due to trial and error, and repeated measurements more than necessary to prevent this may occur.

In addition, various devices that provide services using map data may be difficult to handle a significant amount of data of a created map, or a load may increase due to a significant amount of data.

Accordingly, there is a need for a technology capable of accurately measuring and collecting indoor environment data to create an indoor map, and to reduce the weight of the created indoor map data.

Korean Registered Patent Publication No. 10-1983785 (2019.05.23)

The present invention automatically travels indoors while acquiring and collecting shape data of the surrounding environment, while controlling the driving route so that the route once traveled is not driven again, and creating an indoor map using the obtained surrounding shape data, It is to provide an indoor topology map generation method for creating an indoor topology map using the created indoor map and an autonomous driving robot for the same.

According to an aspect of the present invention, a method for generating an indoor topology map performed by an autonomous driving robot equipped with a Lidar sensor is disclosed.

In the indoor topology map generation method according to an embodiment of the present invention, starting driving indoors, acquiring peripheral shape data using the lidar sensor, and checking a loop closure for a host travel route. The step of performing, setting the scheduled traveling route so that the host route and the scheduled traveling route do not intersect according to the result of the closed loop inspection, and when the traveling is finished, an indoor map is generated using the obtained surrounding shape data. And generating an indoor topology map using the generated indoor map.

The performing of a loop closure test for the host travel path includes: determining a main point by analyzing the peripheral shape data acquired in real time while acquiring the peripheral shape data while driving, the The steps of creating the host route by setting the determined main point as a node and connecting the set node with a line, creating the host route and at the same time, a new node newly created according to the determination of a new main point and a preset Calculating the Euclidean distance of a node, and when there is a preset node whose calculated Euclidean distance is less than or equal to a preset threshold distance, a new node overlaps the position of a preset node that is less than or equal to the threshold distance. And determining that the closed loop is formed.

In the step of setting the scheduled traveling route, the node newly set at the time when the closed loop is detected is deleted from the original traveling route, and the location of the new node and the preset node forming the closed loop is avoided, and the traveling schedule is performed. Set the path.

The generating of the indoor topology map may include dividing the indoor map into areas of a preset size based on a driving route displayed on the indoor map, extracting morphological feature information of the divided areas, and the extraction Comparing the previously set morphological feature information with the morphological feature information of a pre-set main point, classifying the divided region into a main point corresponding to the extracted morphological feature information, and the classification according to the comparison result And generating the indoor topology map by setting the main point as a node.

The main points include the indoor room, corridor, and entrance, wherein the morphological characteristic information of the room is expressed in a form that approximates the surface representing the shape of the room, and the morphological characteristic information of the corridor represents the corridor shape. It is expressed by including a long shaft shape, and the morphological characteristic information of the entrance is expressed by including a narrow gap shape representing the entrance shape and a shape approximating a surface representing the shape of a room or corridor connected to both sides of the narrow gap, respectively. .

According to another aspect of the present invention, an autonomous driving robot equipped with a Lidar sensor is disclosed.

An autonomous driving robot according to an embodiment of the present invention includes a memory storing a command and a processor that executes the command, wherein the command is a step of starting driving indoors, and peripheral shape data using the lidar sensor. Acquiring, performing a loop closure test for a host traveling route, setting the scheduled traveling route so that the host traveling route and the scheduled traveling route do not intersect according to the closed loop inspection result, When the driving is finished, an indoor topology map generating method including generating an indoor map using the obtained surrounding shape data and generating an indoor topology map using the generated indoor map is performed.

An indoor topology map generation method and an autonomous driving robot for the same according to an embodiment of the present invention, while automatically driving indoors, acquires and collects shape data of the surrounding environment, and does not re-drive the route once traveled. By controlling the driving route so that it is not possible, and by creating an indoor map using the acquired surrounding shape data, and creating an indoor topology map using the created indoor map, it is possible to create an indoor map and indoor topology map with high accuracy. It is possible to lighten the data of the indoor map.

1 is a diagram schematically illustrating an operation of an autonomous driving robot according to an embodiment of the present invention to collect ambient environment data for creating an indoor map indoors.
2 is a flowchart illustrating a method of generating an indoor topology map performed by an autonomous driving robot according to an embodiment of the present invention.
Figure 3 is a flow chart showing the detailed steps of step S300 of Figure 2;
Figure 4 is a flow chart showing the detailed steps of step S600 of Figure 2;
5 to 10 are views for explaining a method of generating an indoor topology map performed by an autonomous driving robot according to an embodiment of the present invention.
11 is a diagram schematically illustrating the configuration of an autonomous driving robot that generates an indoor topology map according to an embodiment of the present invention.

Singular expressions used in the present specification include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as “consisting of” or “comprising” should not be construed as necessarily including all of the various elements or various steps described in the specification, and some of the elements or some steps It may not be included, or it should be interpreted that it may further include additional components or steps. In addition, terms such as "... unit" and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented as hardware or software or a combination of hardware and software. .

Hereinafter, various embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram schematically illustrating an operation of an autonomous driving robot according to an embodiment of the present invention to collect ambient environment data to create an indoor map indoors.

Referring to FIG. 1, the autonomous driving robot 100 according to an embodiment of the present invention has an end point (END) from a start point (START) through room 1, corridor 1, corridor 2, and room 2 in order to create an indoor map. By moving to, it moves around the room evenly and acquires and collects peripheral shape data.

Here, the autonomous driving robot 100 is equipped with a Lidar sensor to acquire peripheral shape data for creating an indoor map, and for autonomous driving, various positioning sensors and driving to measure a position by themselves indoors. It can be equipped with a device.

Thus, the autonomous driving robot 100 may acquire and collect peripheral shape data while driving through a lidar sensor while driving indoors using various positioning sensors and driving devices.

For example, the autonomous driving robot 100 rotates the lidar 360 degrees counterclockwise in a two-dimensional plane and emits a pulsed laser to the outside through the lidar to collect peripheral shape data based on its current position. can do. That is, the autonomous driving robot 100 may receive the pulsed laser reflected from the surrounding object through the lidar, and recognize that the object exists at a certain distance in a certain direction from its own position. In more detail, the autonomous driving robot 100 acquires a first point group by performing measurement using a lidar at a first point, and performing measurement using a lidar at a second point while moving again to obtain a second point group. You can repeat what you do until you're done driving. Thereafter, the autonomous driving robot 100 may accumulate and merge a plurality of point groups measured at each location to generate a 3D map composed of the point groups.

Again, referring to FIG. 1, the autonomous driving robot 100 starts from a starting point, passes through an entrance, and enters corridor 1. Subsequently, the autonomous driving robot 100 moves through the corridor 1 and then passes through the entrance and enters the corridor 2. Subsequently, the autonomous driving robot 100 travels through the corridor 2, passes through the entrance, enters the room 2, and then runs through the room 2 and stops at the end point.

At this time, the autonomous driving robot 100 may determine the main point 10 while driving, set the determined main point 10 as a node, and generate a host route by connecting the set nodes with a line.

Here, the main point 10 may be a peripheral point of an entrance between the room 1 and the corridor 1, between the corridor 1 and the corridor 2, and between the corridor 2 and the room 2, as shown in FIG. 1. The autonomous driving robot 100 may detect an entrance or exit indoors by using the surrounding shape data acquired through a lidar sensor.

Based on the host travel path generated through the determination of the main point 10 as described above, the autonomous driving robot 100 can set its own planned driving path so that the host travel path and the planned driving path do not intersect. An indoor map can be created using the finally acquired surrounding shape data.

In addition, the autonomous driving robot 100 sets the main point detected from the created indoor map as a node, and connects the set nodes according to the driving route from the driving start point to the driving end point generated according to the driving end You can create a map.

2 is a flowchart showing a method of generating an indoor topology map performed by an autonomous robot according to an embodiment of the present invention, FIG. 3 is a flowchart showing detailed steps of step S300 of FIG. 2, and FIG. 4 is a step S600 of FIG. Is a flowchart showing detailed steps of, and FIGS. 5 to 10 are diagrams for explaining a method of generating an indoor topology map performed by an autonomous driving robot according to an embodiment of the present invention. Hereinafter, a method of generating an indoor topology map according to an exemplary embodiment of the present invention will be described with reference to FIGS. 5 to 10 with reference to FIGS. 2 to 4.

In step S100, the autonomous driving robot 100 starts autonomous driving indoors in order to acquire peripheral shape data for creating an indoor map.

For example, the autonomous driving robot 100 may travel indoors using various positioning sensors and driving devices according to a stored autonomous driving algorithm.

In step S200, the autonomous driving robot 100 acquires peripheral shape data using a lidar sensor while driving indoors.

In step S300, the autonomous driving robot 100 performs a loop closure test in order to set its own planned driving path so that the host driving path and the scheduled driving path do not intersect.

For example, FIG. 5 shows an example in which a closed loop is formed on the entire driving path after the autonomous driving robot 100 completes driving from a start point to an end point. When the autonomous driving robot 100 does not detect that a closed loop is formed in the driving path as shown in FIG. 5 and the driving is completed with a closed loop formed in the driving path, the surroundings obtained with the closed loop are formed. Indoor maps created using shape data may be inaccurate. Accordingly, the autonomous driving robot 100 must check whether a closed loop is formed so that a closed loop is not formed on the driving path, and when the closed loop is formed, the scheduled driving path must be corrected.

Hereinafter, a closed loop inspection method according to an embodiment of the present invention will be described with reference to FIG. 3.

In step S310, the autonomous driving robot 100 acquires peripheral shape data using a lidar sensor while driving indoors, and analyzes the peripheral shape data acquired in real time to determine a major point. That is, the autonomous driving robot 100 may determine the main point by comparing the surrounding shape data acquired in real time with the preset main point shape information.

For example, FIG. 9 shows an example of the shape information of main points in the room expressed as peripheral shape data acquired using a lidar sensor. Referring to FIG. 9, the main points may be rooms, corridors, entrances, etc. indoors. Here, the main point shape information 30 of the room may be expressed in a form that approximates the plane representing the shape of the room, and the main point shape information 40 of the corridor may be expressed including a long axis shape indicating the corridor shape. , The main point shape information 50 of the entrance may be expressed by including a narrow gap shape representing the shape of the entrance and a shape approximating a surface representing the shape of a room or corridor connected to both sides of the narrow gap, respectively.

According to an embodiment of the present invention, the main point used to perform the closed-loop inspection may be determined around an entrance where movement from one space to another, such as between a room and a corridor or between a first and a second corridor. have. For example, as shown in FIG. 1, the autonomous driving robot 100 identifies the entrance using the pre-set key point type information 50, and then determines each point before and after passing the entrance as a major point. I can. That is, the autonomous driving robot 100 can set major points in the room 1 and the corridor 1, respectively, centering on the entrance between the room 1 and the corridor 1, and similarly, the corridor 1 and the corridor 1 and Main points can be set for each corridor 2, and main points can be set for corridor 2 and room 2, centering on the entrance between corridor 2 and room 2.

In step S320, the autonomous driving robot 100 sets the determined main point as a node and connects the set node with a line to generate a host travel path.

In step S330, the autonomous driving robot 100 generates a host travel path and calculates a distance between a newly created new node and a preset node according to the determination of a new main point. For example, when a node is set, the autonomous robot 100 may assign a coordinate value to a node to be set, and the Euclidean distance between a new node and a preset node is determined by using the assigned coordinate value. Can be calculated.

In step S340, the autonomous driving robot 100 determines whether the distance between the new node calculated for each preset node and the preset node is less than or equal to a preset threshold distance. Here, the critical distance may be set as a position error value of measured position values determined to be substantially the same position.

In step S350, if there is a preset node whose distance between the calculated new node and the preset node is less than or equal to a preset threshold distance, the new node is duplicated at the location of the preset node and closed loop. It can be determined that is formed.

In step S360, when the calculated distance between the new node and the preset node exceeds the preset threshold distance for all preset nodes, the autonomous driving robot 100 is generated at a new location and the closed loop is not formed. It can be judged as.

For example, referring to FIG. 6, the autonomous driving robot 100 may drive while sequentially setting nodes 1 to 18. In this case, the autonomous driving robot 100 may calculate a Euclidean distance between a new node and a preset node. That is, when generating node 4, the autonomous driving robot 100 calculates the Euclidean distance of each of the new node, node 4, and the preset node, nodes 1, 2, and 3, and calculates the Euclidean distance. Among nodes 2 and 3, it is possible to determine whether a closed loop is formed by checking whether there is a node whose Euclidean distance from the node 4 is less than or equal to the critical distance. As shown in FIG. 6, if, when the 15th node and the 16th node are set, the distance between the 15th node and the 1st node and the 16th node and the 2nd node is less than the threshold distance, the autonomous driving robot 100 ) Can be determined that a closed loop has been detected. Thus, the autonomous driving robot 100 corrects the host travel path by deleting nodes 15 and 16, and avoids the positions of nodes 15 and 16 (that is, the positions of nodes 1 and 2), And in order to create node 18, it is possible to modify and set the planned driving route.

Again, a method of generating an indoor topology map according to an embodiment of the present invention will be described with reference to FIG. 2.

In step S400, the autonomous driving robot 100 sets a scheduled driving route according to the closed loop test result. That is, the autonomous driving robot 100 deletes the newly set node from the host travel path at the time when the closed loop is detected, and avoids the location of the new node and the preset node that formed the closed loop, and can set the expected driving path. .

In step S500, the autonomous driving robot 100 generates an indoor map by using the surrounding shape data finally obtained after the driving is finished.

For example, the peripheral shape data acquired through the lidar sensor may be acquired as a raw map image as shown in FIG. 7. In addition, the autonomous driving robot 100 may generate an indoor map as shown in FIG. 8 by removing noise from the surrounding shape data acquired as a raw map image. In this case, as shown in FIG. 8, the autonomous driving robot 100 may display a driving route 20 from a driving start point to a driving end point generated according to the end of driving on an indoor map.

In step S600, the autonomous driving robot 100 generates an indoor topology map using the generated indoor map.

Hereinafter, a method of generating an indoor topology map using an indoor map according to an embodiment of the present invention will be described with reference to FIG. 4.

In step S610, the autonomous driving robot 100 divides the indoor map into areas of a preset size around the driving route 20 displayed on the indoor map.

In step S620, the autonomous driving robot 100 extracts morphological feature information of the divided area.

In step S630, the autonomous driving robot 100 compares the extracted morphological feature information with the pre-set morphological feature information of a major point.

In step S640, the autonomous driving robot 100 classifies each divided area into main points corresponding to the extracted morphological feature information according to the comparison result.

For example, FIG. 9 shows an example of morphological feature information of major indoor points expressed as ambient morphology data acquired using a lidar sensor. Referring to FIG. 9, the main points may be rooms, corridors, entrances, etc. indoors. Here, the morphological characteristic information 30 of the room can be expressed in a form that approximates the plane representing the shape of the room, and the morphological characteristic information 40 of the corridor can be expressed including a long axis shape representing the corridor shape. , The morphological feature information 50 of the entrance may be expressed by including a narrow gap shape representing the shape of the entrance and a shape approximating a surface representing the shape of a room or corridor connected to both sides of the narrow gap, respectively. Therefore, the autonomous driving robot 100 classifies areas corresponding to rooms, corridors, and entrances among the divided areas into major points of rooms, corridors, and entrances, and areas not corresponding to rooms, corridors, and entrances are not main points. It can be classified into the rest of the area or ignored without being classified.

In step S650, the autonomous driving robot 100 creates an indoor topology map by setting the classified main points as nodes. That is, the autonomous driving robot 100 may generate an indoor topology map by connecting the set node with a line according to the driving path 20 displayed on the indoor map. At this time, each node may display identification information indicating attributes of classified main points such as a room, a corridor, or an entrance.

For example, the autonomous driving robot 100 may generate an indoor topology map as shown in FIG. 10. Referring to FIG. 10, identification information displayed on a node classified as a room includes an alphabet R (Room) indicating a room, and identification information displayed on a node classified as a hallway is an alphabet H (Hallway) indicating a hallway. Including, the identification information displayed on the node classified as an entrance may include an alphabet D (Doorway) indicating an entrance. In addition, in order to distinguish nodes having the same attribute, as illustrated in FIG. 10, the identification information may further include serial numbers assigned to nodes having the same attribute.

11 is a diagram schematically illustrating a configuration of an autonomous driving robot that generates an indoor topology map according to an embodiment of the present invention.

11, the autonomous driving robot 100 according to an embodiment of the present invention includes a processor 110, a memory 120, a sensor unit 130, a driving unit 140, a communication unit 150, and an interface unit ( 160).

The processor 110 may be a CPU or a semiconductor device that executes processing instructions stored in the memory 120.

The memory 120 may include various types of volatile or nonvolatile storage media. For example, the memory 120 may include ROM, RAM, or the like.

For example, the memory 120 may store instructions for performing the indoor topology map generation method according to an embodiment of the present invention. In addition, the memory 120 may store an autonomous driving algorithm of the autonomous driving robot 100 according to an embodiment of the present invention.

The sensor unit 130 may include a lidar sensor that acquires peripheral shape data for creating an indoor map, and includes various positioning sensors for the autonomous driving robot 100 to measure a location indoors for autonomous driving. can do.

The driving unit 140 is a means for driving the autonomous driving robot 100.

The communication unit 150 is a means for transmitting and receiving data with other devices through a communication network.

The interface unit 160 may include a network interface and a user interface for accessing a network.

Meanwhile, the constituent elements of the above-described embodiment can be easily grasped from a process point of view. That is, each component can be identified as a respective process. In addition, the process of the above-described embodiment can be easily grasped from the viewpoint of the components of the device.

In addition, the above-described technical contents may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiments, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

The above-described embodiments of the present invention are disclosed for the purpose of illustration, and those skilled in the art who have ordinary knowledge of the present invention will be able to make various modifications, changes, and additions within the spirit and scope of the present invention. It should be seen as falling within the scope of the following claims.

10: main points
100: autonomous driving robot
110: processor
120: memory
130: sensor unit
140: driving unit
150: communication department
160: interface unit

Claims (6)

In the indoor topology map generation method performed by an autonomous driving robot equipped with a lidar sensor,
Starting driving indoors;
Acquiring peripheral shape data using the lidar sensor;
Determining a first main point by comparing the peripheral shape data acquired in real time with the pre-set main point shape information while acquiring the peripheral shape data while driving;
Setting the determined first major point as a node and connecting the set node with a line to create a host route;
Generating the host travel path and calculating a Euclidean distance between a newly created new node and a preset node according to a determination of a new first major point;
Determining that a new node is duplicated at a position of a predetermined node that is less than or equal to the threshold distance to form a closed loop when there is a predetermined node whose calculated Euclidean distance is less than or equal to a preset threshold distance;
Setting the scheduled traveling route so that the host traveling route and the scheduled traveling route do not intersect according to the formation of the closed loop;
When the driving is finished, generating an indoor map using the acquired surrounding shape data;
Dividing the indoor map into areas of a preset size around a driving route displayed on the indoor map;
Extracting morphological feature information of the divided area;
Comparing the extracted morphological feature information with pre-set morphological feature information of a second main point;
Classifying the divided area into a second main point corresponding to the extracted morphological feature information according to the comparison result; And
Including the step of generating the indoor topology map by setting the classified second main point as a node,
The first main point is the entrance,
The main point shape information of the entrance is expressed including a narrow gap shape representing the shape of the entrance and a shape approximating a surface representing the shape of a room or corridor connected to both sides of the narrow gap, respectively,
The step of determining the first main point,
Identifying the entrance by using the main point type information of the entrance; And
Including the step of determining each point before and after passing through the entrance as the first main point,
The step of setting the scheduled driving route,
A node newly set at the time when the closed loop is detected is deleted from the host travel path, and the new node forming the closed loop and the position of a preset node are avoided to set the scheduled travel path,
The step of generating the indoor map,
generating the indoor map by removing noise from the surrounding shape data acquired as a raw map image; And
And displaying a driving route from a driving start point to a driving end point generated according to the completion of the driving on the indoor map,
The second main point includes the indoor room, the corridor, and the entrance,
The morphological characteristic information of the room is expressed in a form that approximates the plane representing the shape of the room,
The morphological feature information of the corridor is expressed by including an elongated shaft shape representing the corridor shape,
The morphological characteristic information of the entrance is expressed by including a shape approximating a shape of a narrow gap representing the shape of the entrance and a surface representing the shape of a room or corridor connected to both sides of the narrow gap, respectively,
Generating the indoor topology map,
Connecting the set node with a line according to the driving route; And
Including the step of displaying identification information indicating the attribute of the second main point classified in the set node,
The identification information includes an alphabet indicating the room, the corridor or the entrance, and a serial number for distinguishing nodes having the same property.
delete delete delete delete In an autonomous driving robot equipped with a Lidar sensor,
A memory for storing an instruction; And
Including a processor that executes the instruction,
The above command is:
Starting driving indoors;
Acquiring peripheral shape data using the lidar sensor;
Determining a first main point by comparing the peripheral shape data acquired in real time with the pre-set main point shape information while acquiring the peripheral shape data while driving;
Setting the determined first major point as a node and connecting the set node with a line to create a host route;
Generating the host travel path and calculating a Euclidean distance between a newly created new node and a preset node according to a determination of a new first major point;
Determining that a new node is duplicated at a position of a predetermined node that is less than or equal to the threshold distance to form a closed loop when there is a predetermined node whose calculated Euclidean distance is less than or equal to a preset threshold distance;
Setting the scheduled traveling route so that the host traveling route and the scheduled traveling route do not intersect according to the formation of the closed loop;
When the driving is finished, generating an indoor map using the acquired surrounding shape data;
Dividing the indoor map into areas of a preset size around a driving route displayed on the indoor map;
Extracting morphological feature information of the divided area;
Comparing the extracted morphological feature information with pre-set morphological feature information of a second main point;
Classifying the divided area into a second main point corresponding to the extracted morphological feature information according to the comparison result; And
Performing an indoor topology map generating method comprising the step of generating the indoor topology map by setting the classified second main point as a node,
The first main point is the entrance,
The main point shape information of the entrance is expressed including a narrow gap shape representing the shape of the entrance and a shape approximating a surface representing the shape of a room or corridor connected to both sides of the narrow gap, respectively,
The step of determining the first main point,
Identifying the entrance by using the main point type information of the entrance; And
Including the step of determining each point before and after passing through the entrance as the first main point,
The step of setting the scheduled driving route,
A node newly set at the time when the closed loop is detected is deleted from the host travel path, and the new node forming the closed loop and the position of a preset node are avoided to set the scheduled travel path,
The step of generating the indoor map,
generating the indoor map by removing noise from the surrounding shape data acquired as a raw map image; And
And displaying a driving route from a driving start point to a driving end point generated according to the completion of the driving on the indoor map,
The second main point includes the indoor room, the corridor, and the entrance,
The morphological characteristic information of the room is expressed in a form that approximates the plane representing the shape of the room,
The morphological feature information of the corridor is expressed by including a long axis shape representing the corridor shape,
The morphological characteristic information of the entrance is expressed by including a narrow gap shape representing the shape of the entrance and a shape approximating a surface representing the shape of a room or corridor connected to both sides of the narrow gap, respectively,
Generating the indoor topology map,
Connecting the set node with a line according to the driving route; And
Including the step of displaying identification information indicating the attribute of the second main point classified in the set node,
The identification information includes an alphabet indicating the room, the corridor or the entrance, and a serial number for distinguishing nodes having the same attribute.

Images