CN113741422B - Robot topology map generation system, method, computer device and storage medium - Google Patents
Robot topology map generation system, method, computer device and storage medium Download PDFInfo
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Abstract
The invention relates to the field of robot navigation, and discloses a system, a method, computer equipment and a storage medium for generating a robot topological map, wherein the system comprises a memory, a processor and computer readable instructions which are stored in the memory and can run on the processor, and the processor executes the computer readable instructions to realize the following steps: acquiring an action track of the robot in a map building scene; filtering the action track to generate a homogenized point track; acquiring depth image data corresponding to the homogenized point track; generating an obstacle distance map of the mapping scene according to the depth image data, and setting a feasible region according to the obstacle distance map; and in the feasible region, processing the action track through a preset algorithm to generate a topological map of the mapping scene. The invention greatly improves the drawing efficiency of the topological map and reduces the artificial error of the topological map.
Description
Technical Field
The present invention relates to the field of robot navigation, and in particular, to a system, a method, a computer device, and a storage medium for generating a topological map of a robot.
Background
In the robot automatic navigation process, a pre-drawn topological map is usually relied on. Topology maps are typically drawn by human beings. However, for complex scenes, the drawing process of the topological map is complicated, time and labor are wasted, and the situation that the topological map is not matched with an actual scene exists, so that the normal operation of the robot is affected.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a system, a method, a computer device and a storage medium for generating a topological map of a robot, so as to improve the efficiency of drawing the topological map and reduce the human error of the topological map.
A robot topology map generation system comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, when executing the computer readable instructions, performing the steps of:
acquiring an action track of the robot in a map building scene;
filtering the action track to generate a homogenized point track;
acquiring depth image data corresponding to the homogenized point track;
generating an obstacle distance map of the mapping scene according to the depth image data, and setting a feasible region according to the obstacle distance map;
and processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene.
A robot topology map generation method, comprising:
acquiring an action track of the robot in a map building scene;
filtering the action track to generate a homogenized point track;
acquiring depth image data corresponding to the homogenized point track;
generating an obstacle distance map of the mapping scene according to the depth image data, and setting a feasible region according to the obstacle distance map;
and processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene.
A computer device comprising a memory, a processor and computer readable instructions stored in the memory and executable on the processor, the processor implementing the robot topology map generation method described above when executing the computer readable instructions.
One or more readable storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform a robot topology map generation method as described above.
According to the system, the method, the computer equipment and the storage medium for generating the topological map of the robot, the action track of the robot in the map building scene is acquired, the acquisition difficulty of the action track is low, and the cost for drawing the topological map is reduced. And filtering the action track to generate a homogenized point track so as to normalize the action track and improve the accuracy of image processing. Depth image data corresponding to the homogenized point trajectory is acquired to determine an obstacle distance. And generating an obstacle distance map of the mapping scene according to the depth image data, and setting a feasible region according to the obstacle distance map so as to ensure that a topological path is in the feasible region. And processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene, wherein the preset algorithm can process the track section of the similar curve into a plurality of track segments so as to form a path of the topological map. According to the invention, the topological map can be automatically generated only by acquiring the action track, so that the drawing efficiency of the topological map is greatly improved, and the artificial error of the topological map is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a robot topology map generation system according to an embodiment of the present invention;
FIG. 2 is a simplified schematic diagram of a robot entering a mapping scene in an embodiment of the present invention;
FIG. 3 is a diagram of an image including homogenized point tracks after filtering according to an embodiment of the present invention;
FIG. 4 is a partial obstacle distance map in accordance with one embodiment of the invention;
FIG. 5 is a topology map that has been inverted in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of a route for generating a topological map according to an action trajectory according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a computer device in accordance with an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the robot topology map generation system comprises a memory, a processor, and computer program computer readable instructions stored in the memory and executable on the processor, wherein the processor is configured to execute the computer readable instructions to implement the steps of:
s10, acquiring an action track of the robot in the map building scene.
Understandably, the mapping scenario may be a robot work scenario, such as a restaurant, hotel, hospital or other indoor and outdoor scenario. The action trajectory may be a walking trajectory of the robot in the diagrammatical scene. The action track comprises a point track of a plurality of robots. In one example, the point tracks may be collected at preset time intervals. The preset time interval can be set according to actual needs. For example, the preset time interval may be 1s to 10s. In other examples, the point tracks may be acquired at preset displacement intervals and/or angular intervals, such as once every 0.2m of movement, once every 20 degrees of change in angle, etc., without limitation of specific values.
In an example, as shown in fig. 2, the mapping scenario may be a restaurant. When the robot enters a restaurant, a worker can push the robot to travel along a pedestrian passageway of the restaurant to form a movement track, and meanwhile, a laser radar on the robot can acquire depth image data along the pedestrian passageway. Thus, the method is repeated for a plurality of times, and a plurality of action tracks can be acquired.
S20, filtering the action track to generate a homogenized point track.
Understandably, in the action track, the time intervals of acquisition of adjacent track points are equal, and the intervals between the adjacent track points have a certain difference. The pitch is related to the speed of movement of the robot over a time interval. The action track is required to be filtered, so that the difference of the intervals is reduced, and the homogenized point track is obtained. The uniform point track means that the distance between adjacent track points is in a distance range, so that the uniform distance is ensured, and the condition of overlarge or undersize does not occur. As shown in fig. 3, fig. 3 is an image including a homogenized point track after being filtered. The linear curve in fig. 3 is a homogenized point track.
S30, acquiring depth image data corresponding to the homogenized point track.
The depth image data may understandably be depth information data acquired by a depth camera and/or a lidar mounted on the robot. The homogenized point track comprises a plurality of track points, and each track point acquires at least one frame of depth image data. The depth image data corresponding to the homogenized point locus refers to a set of depth image data acquired at each locus point.
S40, generating an obstacle distance map of the mapping scene according to the depth image data, and setting a feasible region according to the obstacle distance map.
Understandably, after the depth image data of each track point is obtained, the depth image data may be stitched according to the positions of the track points to form a three-dimensional point cloud (obstacle point). A two-dimensional obstacle map may be generated by projecting a three-dimensional point cloud onto a ground plane (which may be a horizontal plane) of the mapping scene. And respectively calculating the minimum distance from each position point to the obstacle point on the initial map. The pixel values of the position points of the initial map are set according to the minimum distance. As shown in fig. 4, the pixel value of the obstacle point may be set to 0, and the pixel values of other position points increase with an increase in the minimum distance to the obstacle point. The lighter the color (the larger the pixel value) in the obstacle distance map, the larger the distance from the obstacle. A limit pixel value may be set and a region where the pixel value is larger than the limit pixel value is set as a feasible region.
In other examples, the pixel value of the obstacle point may be set to 255, with the pixel values of other points decreasing with increasing minimum distance to the obstacle point. The darker the color (the larger the pixel value) in the obstacle distance map, the greater the distance from the obstacle. A limit pixel value may be set and a region where the pixel value is smaller than the limit pixel value is set as a feasible region.
And S50, processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene.
The preset algorithm is understandably an algorithm that approximately represents a curve as a series of points and reduces the number of points. The algorithm has translational invariance and rotational invariance, and after a curve and a threshold value are given, the sampling result is certain. Here, the action trajectory is segmented by several trajectories, which may be represented by curves. The tracks can be piecewise linearized through processing of a preset algorithm to generate a plurality of track segments. And connecting all track segments to form a path formed by a plurality of straight line segments, namely, a topological map of the mapping scene. When drawing a topological map, it is necessary to ensure that any point on all paths is within a feasible region. If any point on all paths is not in the feasible region, the topology path needs to be re-planned until any point on all paths is in the feasible region. As shown in fig. 5, fig. 5 is an inverted topological map.
In the steps S10-S50, the action track of the robot in the map construction scene is acquired, and the acquisition difficulty of the action track is low, so that the cost for drawing the topological map is reduced. And filtering the action track to generate a homogenized point track so as to normalize the action track and improve the accuracy of image processing. Depth image data corresponding to the homogenized point trajectory is acquired to determine an obstacle distance. And generating an obstacle distance map of the mapping scene according to the depth image data, and setting a feasible region according to the obstacle distance map so as to ensure that a topological path is in the feasible region. And processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene, wherein the preset algorithm can process the track section of the similar curve into a plurality of track segments so as to form a path of the topological map. In the embodiment, the topological map can be automatically generated only by acquiring the action track, so that the drawing efficiency of the topological map is greatly improved, and the artificial error of the topological map is reduced.
Optionally, in step S20, that is, the filtering processing is performed on the action track to generate a homogenized point track, including:
s201, interpolating the action track, and then performing median filtering and mean smoothing to generate the homogenized point track; in the homogenized point track, the distance between adjacent track points is larger than a first threshold value and smaller than a second threshold value.
It can be appreciated that the action track may be interpolated to increase the number of track points such that the spacing between all adjacent track points is less than the first threshold. And removing track points with too small intervals through median filtering, and performing mean smoothing treatment to ensure that the intervals of adjacent track points are approximately equal to each other, thereby obtaining a homogenized track. In the homogenized dot track, the distance between adjacent track points is larger than a first threshold value and smaller than a second threshold value.
Optionally, the first threshold comprises 0.25m and the second threshold comprises 1m.
It is understood that the first threshold and the second threshold may be set according to actual needs. In one example, the first threshold comprises 0.25m and the second threshold comprises 1m.
Optionally, step S40, that is, generating the obstacle distance map of the mapping scene according to the depth image data, includes:
s401, splicing the depth image data according to the position of the robot in the homogenizing point track, and generating an obstacle map containing the positions of the obstacles;
s402, calculating a distance value between the idle position and the obstacle position, and rendering the obstacle map according to the distance value to generate the obstacle distance map.
Understandably, the depth image data on each track point can be spliced according to the position of the track point to form a three-dimensional point cloud containing the obstacle point. A two-dimensional obstacle map may be generated by projecting a three-dimensional point cloud onto a ground plane (which may be a horizontal plane) of the mapping scene. The location points of the obstacle locations may be marked with black. The idle position refers to a position point where there is no obstacle. The distance value of the idle position from the obstacle position may refer to the distance of the idle position from the nearest neighboring obstacle position, i.e. the minimum distance. Each free position may obtain a unique distance value. The pixel value of the idle position may be set according to the distance value. In one example, the pixel value of a location point greater than or 2 meters from the obstacle may be set to 255 (i.e., white). As shown in fig. 4, fig. 4 is a local obstacle distance map in an example. Fig. 4 contains three obstacle points. Wherein the pixel value of the obstacle point is set to 0, and the pixel values of other position points are increased along with the increase of the distance from the obstacle point.
Optionally, step S40, that is, the generating an obstacle distance map of the mapping scene according to the depth image data, setting a feasible region according to the obstacle distance map, includes:
s403, acquiring a limiting distance of the obstacle;
s404, obtaining a limit pixel value corresponding to the limit distance;
s405, if the pixel value of the obstacle is 0, setting an area with the pixel value larger than the limit pixel value in the obstacle distance map as the feasible area;
and S406, if the pixel value of the obstacle is 255, setting the area with the pixel value smaller than the limit pixel value in the obstacle distance map as the feasible area.
Understandably, the limiting distance is the minimum distance between the robot and the obstacle. The limiting distance can be set according to actual needs. In an example, the limiting distance may be set to 0.5m. A correspondence between the distance and the pixel value may be set. For example, the pixel value of the position where the obstacle is (the distance is 0) is set to be 0, the pixel values of the positions where the obstacle is 3m and 3m are set to be 255, the pixel value of the position where the distance is between 0 and 3m is positively correlated with the distance, and the pixel value can be changed linearly or can be changed nonlinearly, and each distance has a uniquely corresponding pixel value.
In one example, in an obstacle distance map, the pixels of the location points increase with increasing distance from the obstacle point. Therefore, the pixel value of the limiting distance can be determined first and recorded as a pixel value threshold, and then the area with the pixel value larger than the pixel value threshold is the feasible area.
In another example, in the obstacle distance map, the pixels of the location points decrease with increasing distance from the obstacle point. Therefore, the pixel value of the limiting distance can be determined first and marked as a pixel value threshold, and then the area with the pixel value smaller than the pixel value threshold is the feasible area.
Optionally, the action track includes a plurality of track segments;
step S50, namely, the step of processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene includes:
s501, acquiring a first track node and a second track node corresponding to track segmentation, and generating a first connecting line between the first track node and the second track node;
s502, calculating the distance between the track point on the track segment and the first connecting line, and setting the track point with the shortest curve distance with the first track node and the distance between the track point and the first connecting line being greater than or equal to the distance threshold as a first intermediate node;
s503, connecting the first intermediate node with the second track node to generate a second connecting line;
s504, calculating the distance between the track point between the first intermediate node and the second track node and the second connecting line, and setting the track point with the shortest curve distance with the first intermediate node and the distance with the second connecting line being greater than or equal to the distance threshold as the second intermediate node;
s505, repeating the steps until the distances between all track points on the residual curves and the corresponding intermediate connecting lines are smaller than a distance threshold;
s506, sequentially connecting the first track node, the intermediate node and the second track node to generate a plurality of track line segments, wherein the intermediate node comprises the first intermediate node and the second intermediate node.
S507, splicing the track segments to generate a topological path, wherein the topological map comprises the topological path.
It is understood that several track segments may be partitioned from the action track. Each track segment may generate at least one track segment. The first track node and the second track node corresponding to the track segment may be acquired from the node setting information, respectively. Here, the first track node and the second track node are two end points of the track segment. Connecting the first track node and the second track node may form a connection line between the first track node and the second track node. And calculating the distances between all track points in the track segment and the connecting line, selecting the track point with the distance larger than the distance threshold and the shortest curve distance with the first track node, and recording the track point as the first intermediate node. Here, the curve distance refers to the length of the curve between two points.
A second connection between the first intermediate node and the first trace node may be formed by connecting the first intermediate node and the first trace node. And calculating the distance between the track point between the first intermediate node and the second track node and the second connecting line, selecting the track point with the distance larger than the distance threshold and the shortest curve distance with the first intermediate node, and marking the track point as the second intermediate node.
The above steps may be repeated to form new intermediate links and intermediate nodes until all the trace points on the remaining curve (the curve after the second intermediate node) have distances from the corresponding intermediate links less than the distance threshold. Specific steps can refer to steps S501-S504, and are not described herein.
The first track node, the intermediate node and the second track node are sequentially connected, and a plurality of track line segments can be generated. If no intermediate node exists, a track segment between the first track node and the second track node may be generated. If there are N intermediate nodes (N is a positive integer), then n+1 track segments can be generated.
Each track segment is spliced according to the sequence, specifically, the track segments can be spliced according to the common track nodes, and mutually communicated topological paths can be formed. At this time, the action track is converted into a topology path, and a topology map is generated.
In an example, as shown in fig. 6, fig. 6 is a schematic diagram of a path for generating a topological map from an action trajectory. In the example of fig. 6, there are included a plurality of track segments represented by curves, DA, DB, DC, respectively. Taking the track segment DA (also denoted as curve DA) as an example, connecting the first track node D and the second track node a may generate a connection DA (denoted by a dashed line in the figure) between them. Starting from the point D, calculating the distance from the point on the curve DA to the connecting line DA, and selecting a track point with the first distance larger than a distance threshold value, namely a first intermediate node H.
Connecting the first intermediate node H and the second trajectory node a, a connection line HA (indicated by a broken line in the figure) therebetween may be generated. Starting from the point H, calculating the distance from the point on the curve HA to the connecting line HA, and selecting a track point with the first distance larger than a distance threshold value as a second intermediate node G.
Connecting the second intermediate node G and the second trajectory node a, a connection GA (indicated by a broken line in the figure) therebetween may be generated. And calculating the distance from the point G to the connecting line GA on the curve GA, and selecting a track point with the first distance larger than a distance threshold value, namely a third intermediate node K.
Connecting the third intermediate node K and the second trace node a may generate a connection KA therebetween. Starting from point K, the distance from the point on the curve KA to the line KA is calculated. Since the distance from the point on the curve to the connection KA is less than the distance threshold, all steps of setting the intermediate node have been completed. At this time, the track line segment DH, HG, GK, KA (schematic diagram of the lower right corner of fig. 6) connected in sequence can be formed by sequentially connecting D, H, G, K, A.
Optionally, after step S507, that is, the splicing the plurality of track segments, a topology path is generated, and after the topology map includes the topology path, the method further includes:
s5071, judging whether the topological path is in the feasible region or not;
s5072, if the topology path is in the feasible region, judging that the topology path is available;
s5073, if the topological path is not in the feasible region, receiving a distance modification instruction;
s5074, adjusting the limiting distance according to the distance modification instruction to obtain a modified limiting distance;
s5075, generating a modified feasible region according to the modified limiting distance.
Understandably, if all path points on the topology path fall within the feasible region, it is determined that the topology path is within the feasible region. And if the partial path points exist on the topological path and are not in the feasible region, judging that the topological path is not in the feasible region. When the topology path is within the feasible region, the topology path does not collide with the feasible region, and thus the topology path is available. At this time, a topology map may be generated in combination with the topology path and the feasible region.
When the topology path is not within the feasible region, the topology path collides with the feasible region, and thus, an adjustment is required to the topology path or the feasible region. In an example, the limit distance may be modified to change the feasible region.
The distance modification instruction may be an instruction entered by a user. Typically, a distance modification instruction is used to reduce the value of the limiting distance. For example, the original limiting distance is 0.5m, and the modified limiting distance is 0.3m. After the modified limiting distance is determined, a modified feasible region may be generated according to the modified limiting distance. In an example, the original feasible region refers to a region with a pixel value greater than 200, and the modified feasible region may be a region with a pixel value greater than 180.
After the modified feasible region is obtained, it may be continued to determine that the topology path is within the modified feasible region. If the topology path is in the modified feasible region, a topology map can be generated by combining the topology path and the modified feasible region. If the topology path is not in the modified feasible region, the limitation distance can be selected to be continuously adjusted or the topology path can be adjusted. In some examples, conflicting path portions may be marked, the topology path may be automatically revised by a path modification program, or the path may be manually revised.
Optionally, after step S507, that is, splicing the plurality of track segments to generate a topology path, the topology map further includes, after the topology path is included:
judging whether the track line segment is in a feasible region, if the track line segment is not in the feasible region, reducing the value of the distance threshold to obtain a new distance threshold, specifically halving the distance threshold, executing the step of processing the action track in the feasible region by using the new distance threshold and generating the topological map of the mapping scene by a preset algorithm. I.e. the steps of S501-507 are repeatedly performed. New track segments are regenerated.
In an actual scene, whether the track line segment is located in a feasible area can also be judged in real time, if not, the distance threshold can be directly adjusted to obtain a new distance threshold, then the new distance threshold is used for regenerating the track line segment, and other track line segments are generated according to the original distance threshold.
The above-described modules in the robot topology map generation system may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In an embodiment, a robot topology map generation method is provided, which corresponds to the robot topology map generation system in the above embodiment one by one. As shown in fig. 1, the method for generating a robot topology map provided in this embodiment includes the following steps:
s10, acquiring an action track of the robot in a diagramming scene;
s20, filtering the action track to generate a homogenized point track;
s30, acquiring depth image data corresponding to the homogenized point track;
s40, generating an obstacle distance map of the mapping scene according to the depth image data, and setting a feasible region according to the obstacle distance map;
s50, processing the action track in the feasible region through a preset algorithm, and generating a topological map of the mapping scene.
Optionally, in step S20, that is, the filtering processing is performed on the action track to generate a homogenized point track, including:
and S201, interpolating the action track, and then performing median filtering and mean smoothing to generate a homogenized point track, wherein the distance between adjacent track points in the homogenized point track is larger than a first threshold and smaller than a second threshold.
Optionally, the first threshold comprises 0.25m and the second threshold comprises 1m.
Optionally, step S40, that is, generating the obstacle distance map of the mapping scene according to the depth image data, includes:
s401, splicing the depth image data according to the position of the robot in the homogenizing point track, and generating an obstacle map containing the positions of the obstacles;
s402, calculating a distance value between the idle position and the obstacle position, and rendering the obstacle map according to the distance value to generate the obstacle distance map.
Optionally, step S40, that is, the generating an obstacle distance map of the mapping scene according to the depth image data, setting a feasible region according to the obstacle distance map, includes:
s403, acquiring a limiting distance of the obstacle;
s404, obtaining a limit pixel value corresponding to the limit distance;
s405, if the pixel value of the obstacle is 0, setting an area with the pixel value larger than the limit pixel value in the obstacle distance map as the feasible area;
and S406, if the pixel value of the obstacle is 255, setting the area with the pixel value smaller than the limit pixel value in the obstacle distance map as the feasible area.
Optionally, the action track includes a plurality of track segments;
step S50, namely, the step of processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene includes:
s501, acquiring a first track node and a second track node corresponding to track segmentation, and generating a first connecting line between the first track node and the second track node;
s502, calculating the distance between the track point on the track segment and the first connecting line, and setting the track point with the shortest curve distance with the first track node and the distance between the track point and the first connecting line being greater than or equal to the distance threshold as a first intermediate node;
s503, connecting the first intermediate node with the second track node to generate a second connecting line;
s504, calculating the distance between the track point between the first intermediate node and the second track node and the second connecting line, and setting the track point with the shortest curve distance with the first intermediate node and the distance with the second connecting line being greater than or equal to the distance threshold as the second intermediate node;
s505, repeating the steps until the distances between all track points on the residual curves and the corresponding intermediate connecting lines are smaller than a distance threshold;
s506, sequentially connecting the first track node, the intermediate node and the second track node to generate a plurality of track line segments, wherein the intermediate node comprises the first intermediate node and the second intermediate node;
s507, splicing the track segments to generate a topological path, wherein the topological map comprises the topological path.
Optionally, after step S507, that is, the splicing the plurality of track segments, a topology path is generated, and after the topology map includes the topology path, the method further includes:
s5071, judging whether the topological path is in the feasible region or not;
s5072, if the topology path is in the feasible region, judging that the topology path is available;
s5073, if the topological path is not in the feasible region, receiving a distance modification instruction;
s5074, adjusting the limiting distance according to the distance modification instruction to obtain a modified limiting distance;
s5075, generating a modified feasible region according to the modified limiting distance.
Optionally, after step S507, that is, the splicing the plurality of track segments, a topology path is generated, and after the topology map includes the topology path, the method further includes:
judging whether the track line segment is in the feasible region or not;
if the track line segment is not in the feasible region, reducing the value of the distance threshold value to obtain a new distance threshold value;
and executing the step of processing the action track in the feasible region by using the new distance threshold value through a preset algorithm to generate a topological map of the mapping scene.
For specific limitations on the robot topology map generation method, reference may be made to the limitations on the robot topology map generation system above, and no further description is given here. It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a readable storage medium, an internal memory. The readable storage medium stores an operating system, computer readable instructions, and a database. The internal memory provides an environment for the execution of an operating system and computer-readable instructions in a readable storage medium. The database of the computer device is used for storing data related to the robot topology map generation method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer readable instructions when executed by a processor implement a robot topology map generation method. The readable storage medium provided by the present embodiment includes a nonvolatile readable storage medium and a volatile readable storage medium.
In one embodiment, a computer device is provided that includes a memory, a processor, and computer readable instructions stored on the memory and executable on the processor, when executing the computer readable instructions, performing the steps of:
acquiring an action track of the robot in a map building scene;
filtering the action track to generate a homogenized point track;
acquiring depth image data corresponding to the homogenized point track;
generating an obstacle distance map of the mapping scene according to the depth image data, and setting a feasible region according to the obstacle distance map;
and in the feasible region, processing the action track through a preset algorithm, and generating a topological map of the mapping scene.
In one embodiment, one or more computer-readable storage media are provided having computer-readable instructions stored thereon, the readable storage media provided by the present embodiment including non-volatile readable storage media and volatile readable storage media. The readable storage medium has stored thereon computer readable instructions which when executed by one or more processors perform the steps of:
acquiring an action track of the robot in a map building scene;
filtering the action track to generate a homogenized point track;
acquiring depth image data corresponding to the homogenized point track;
generating an obstacle distance map of the mapping scene according to the depth image data, and setting a feasible region according to the obstacle distance map;
and in the feasible region, processing the action track through a preset algorithm, and generating a topological map of the mapping scene.
Those skilled in the art will appreciate that implementing all or part of the above described embodiment methods may be accomplished by instructing the associated hardware by computer readable instructions stored on a non-volatile readable storage medium or a volatile readable storage medium, which when executed may comprise the above described embodiment methods. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. A robot topology map generation system comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, wherein the processor is configured to execute the computer readable instructions to perform the steps of:
acquiring an action track of the robot in a map building scene;
filtering the action track to generate a homogenized point track;
acquiring depth image data corresponding to the homogenized point track;
generating an obstacle distance map of the mapping scene according to the depth image data, wherein the obstacle distance map comprises: splicing the depth image data according to the position of the robot in the homogenizing point track, and generating an obstacle map containing the positions of the obstacles; calculating a distance value between an idle position and an obstacle position, and rendering the obstacle map according to the distance value to generate the obstacle distance map;
setting a feasible region according to the obstacle distance map, including: acquiring a limiting distance of an obstacle; acquiring a limiting pixel value corresponding to the limiting distance; if the pixel value of the obstacle is 0, setting a region with the pixel value larger than the limit pixel value in the obstacle distance map as the feasible region; if the pixel value of the obstacle is 255, setting a region with the pixel value smaller than the limit pixel value in the obstacle distance map as the feasible region;
processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene;
the action track comprises a plurality of track segments;
and processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene, wherein the topological map comprises the following steps:
acquiring a first track node and a second track node corresponding to track segmentation, and generating a first connecting line between the first track node and the second track node;
calculating the distance between the track point on the track segment and the first connecting line, and setting the track point with the shortest curve distance with the first track node and the distance between the track point and the first connecting line being greater than or equal to a distance threshold as a first intermediate node;
connecting the first intermediate node with the second track node to generate a second connecting line;
calculating the distance between the track point between the first intermediate node and the second track node and the second connecting line, and setting the track point with the shortest curve distance with the first intermediate node and the distance with the second connecting line being greater than or equal to the distance threshold as the second intermediate node;
repeating the steps until the distances between all the track points on the residual curves and the corresponding intermediate connecting lines are smaller than the distance threshold value;
the first track node, the intermediate node and the second track node are sequentially connected to generate a plurality of track line segments, and the intermediate node comprises the first intermediate node and the second intermediate node;
and splicing the track segments to generate a topological path, wherein the topological map comprises the topological path.
2. The robot topology map generation system of claim 1, wherein said filtering said action trajectories to generate homogenized point trajectories comprises:
and interpolating the action track, and then performing median filtering and mean smoothing to generate a homogenized point track, wherein the distance between adjacent track points in the homogenized point track is larger than a first threshold and smaller than a second threshold.
3. The robotic topology map generation system of claim 2, wherein the first threshold comprises 0.25m and the second threshold comprises 1m.
4. The robotic topology map generation system of claim 1, wherein said concatenating said plurality of trajectory segments generates a topology path, said topology map comprising said topology path, further comprising, after:
judging whether the topological path is in the feasible region or not;
if the topology path is in the feasible region, judging that the topology path is available;
if the topology path is not in the feasible region, receiving a distance modification instruction;
adjusting the limiting distance according to the distance modification instruction to obtain a modified limiting distance;
and generating a modified feasible region according to the modified limiting distance.
5. The robotic topology map generation system of claim 1, wherein said concatenating said plurality of trajectory segments generates a topology path, said topology map comprising said topology path, further comprising, after:
judging whether the track line segment is in the feasible region or not;
if the track line segment is not in the feasible region, reducing the value of the distance threshold value to obtain a new distance threshold value;
and executing the step of processing the action track in the feasible region by using the new distance threshold value through a preset algorithm to generate a topological map of the mapping scene.
6. A robot topology map generation method, comprising:
acquiring an action track of the robot in a map building scene;
filtering the action track to generate a homogenized point track;
acquiring depth image data corresponding to the homogenized point track;
generating an obstacle distance map of the mapping scene according to the depth image data, wherein the obstacle distance map comprises: splicing the depth image data according to the position of the robot in the homogenizing point track, and generating an obstacle map containing the positions of the obstacles; calculating a distance value between an idle position and an obstacle position, and rendering the obstacle map according to the distance value to generate the obstacle distance map;
setting a feasible region according to the obstacle distance map, including: acquiring a limiting distance of an obstacle; acquiring a limiting pixel value corresponding to the limiting distance; if the pixel value of the obstacle is 0, setting a region with the pixel value larger than the limit pixel value in the obstacle distance map as the feasible region; if the pixel value of the obstacle is 255, setting a region with the pixel value smaller than the limit pixel value in the obstacle distance map as the feasible region;
processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene;
the action track comprises a plurality of track segments;
and processing the action track in the feasible region through a preset algorithm to generate a topological map of the mapping scene, wherein the topological map comprises the following steps:
acquiring a first track node and a second track node corresponding to track segmentation, and generating a first connecting line between the first track node and the second track node;
calculating the distance between the track point on the track segment and the first connecting line, and setting the track point with the shortest curve distance with the first track node and the distance between the track point and the first connecting line being greater than or equal to a distance threshold as a first intermediate node;
connecting the first intermediate node with the second track node to generate a second connecting line;
calculating the distance between the track point between the first intermediate node and the second track node and the second connecting line, and setting the track point with the shortest curve distance with the first intermediate node and the distance with the second connecting line being greater than or equal to the distance threshold as the second intermediate node;
repeating the steps until the distances between all the track points on the residual curves and the corresponding intermediate connecting lines are smaller than the distance threshold value;
the first track node, the intermediate node and the second track node are sequentially connected to generate a plurality of track line segments, and the intermediate node comprises the first intermediate node and the second intermediate node;
and splicing the track segments to generate a topological path, wherein the topological map comprises the topological path.
7. The method of generating a topological map of a robot according to claim 6, wherein the filtering the action trajectories to generate homogenized point trajectories includes:
and interpolating the action track, and then performing median filtering and mean smoothing to generate a homogenized point track, wherein the distance between adjacent track points in the homogenized point track is larger than a first threshold and smaller than a second threshold.
8. The method for generating a topology map of a robot of claim 6, wherein said concatenating said plurality of trajectory segments generates a topology path, said topology map comprising said topology path, further comprising, after:
judging whether the topological path is in the feasible region or not;
if the topology path is in the feasible region, judging that the topology path is available;
if the topology path is not in the feasible region, receiving a distance modification instruction;
adjusting the limiting distance according to the distance modification instruction to obtain a modified limiting distance;
and generating a modified feasible region according to the modified limiting distance.
9. A computer device comprising a processor which when executing computer readable instructions implements the robot topology map generation method of any of claims 6 to 8.
10. One or more readable storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the robot topology map generation method of any of claims 6 to 8.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100611328B1 (en) * | 2005-06-30 | 2006-08-11 | 고려대학교 산학협력단 | Method and apparatus for building of a thinning-based topological map, method and apparatus for building of thinning-based topological map using topological exploration for a mobile robot |
| CN106643721A (en) * | 2016-10-11 | 2017-05-10 | 北京工业大学 | Construction method of environmental topological map |
| CN107329476A (en) * | 2017-08-02 | 2017-11-07 | 珊口（上海）智能科技有限公司 | A kind of room topology map construction method, system, device and sweeping robot |
| WO2018121448A1 (en) * | 2016-12-30 | 2018-07-05 | 深圳市杉川机器人有限公司 | Topology map creation method and navigation method for mobile robot, programmable device, and computer readable medium |
| CN110361009A (en) * | 2019-07-12 | 2019-10-22 | 深圳市银星智能科技股份有限公司 | A kind of paths planning method, path planning system and mobile robot |
| CN111158384A (en) * | 2020-04-08 | 2020-05-15 | 炬星科技（深圳）有限公司 | Robot mapping method, device and storage medium |
| CN111366163A (en) * | 2018-12-25 | 2020-07-03 | 北京欣奕华科技有限公司 | Topological map processing method and device and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108572646A (en) * | 2018-03-19 | 2018-09-25 | 深圳悉罗机器人有限公司 | The rendering method and system of robot trajectory and environmental map |
| KR102466940B1 (en) * | 2018-04-05 | 2022-11-14 | 한국전자통신연구원 | Topological map generation apparatus for traveling robot and method thereof |
-
2021
- 2021-07-30 CN CN202110874201.4A patent/CN113741422B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100611328B1 (en) * | 2005-06-30 | 2006-08-11 | 고려대학교 산학협력단 | Method and apparatus for building of a thinning-based topological map, method and apparatus for building of thinning-based topological map using topological exploration for a mobile robot |
| CN106643721A (en) * | 2016-10-11 | 2017-05-10 | 北京工业大学 | Construction method of environmental topological map |
| WO2018121448A1 (en) * | 2016-12-30 | 2018-07-05 | 深圳市杉川机器人有限公司 | Topology map creation method and navigation method for mobile robot, programmable device, and computer readable medium |
| CN107329476A (en) * | 2017-08-02 | 2017-11-07 | 珊口（上海）智能科技有限公司 | A kind of room topology map construction method, system, device and sweeping robot |
| CN111366163A (en) * | 2018-12-25 | 2020-07-03 | 北京欣奕华科技有限公司 | Topological map processing method and device and storage medium |
| CN110361009A (en) * | 2019-07-12 | 2019-10-22 | 深圳市银星智能科技股份有限公司 | A kind of paths planning method, path planning system and mobile robot |
| CN111158384A (en) * | 2020-04-08 | 2020-05-15 | 炬星科技（深圳）有限公司 | Robot mapping method, device and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 基于细化算法的移动机器人拓扑地图创建;王娜;马昕;;计算机技术与发展(第10期);全文 * |
| 王娜 ; 马昕 ; .基于细化算法的移动机器人拓扑地图创建.计算机技术与发展.2009,(第10期),全文. * |
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