Method and device for positioning carrier, robot and storage medium

Abstract

The invention relates to a method and device for positioning a carrier, a robot and a storage medium, and the method comprises the steps that the robot is controlled to move towards a preset initial position of a target carrier, and the target carrier comprises at least one supporting leg; acquiring a first point cloud scanned by the robot; when it is determined that the robot scans the target carrier according to the first point cloud, a second point cloud of each supporting leg is determined according to the first point cloud; and determining current position information of the target carrier according to the second point cloud of each supporting leg. According to the invention, the robot can position the target carrier, the arrangement of a camera on the robot and the arrangement of a two-dimensional code on the target carrier are avoided, and the hardware cost is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of robots, and in particular, to a method, a device, a robot, and a storage medium for positioning a vehicle carrier. Background Art

[0002] With the development of automation technology, robots are increasingly used in industrial production. In industrial production, goods are usually loaded into a cage truck. After the cage truck is loaded with goods, the robot moves to the position of the cage truck to lift the cage truck, so that the robot can carry the cage truck and then carry the goods in the cage truck to the destination, improving the efficiency of industrial production. Currently, in order to enable the robot to accurately lift the cage truck, a two-dimensional code is usually set on the cage truck and a camera is set on the robot. When the robot reaches the preset position of the cage truck, the two-dimensional code on the cage truck is scanned by the camera on the robot to achieve accurate positioning of the cage truck by the robot. However, the above cage truck positioning method requires both a camera on the robot and a two-dimensional code on the cage truck, increasing the hardware cost. Summary of the Invention

[0003] In view of this, to solve the above technical problems or some of the technical problems, embodiments of the present invention provide a method, a device, a robot, and a storage medium for positioning a vehicle carrier.

[0004] In a first aspect, the present application provides a method for positioning a vehicle carrier, which is applied to a robot. The method includes:

[0005] Controlling the robot to move to a preset initial position of a target vehicle carrier, where the target vehicle carrier includes at least one support leg;

[0006] Obtaining a first point cloud scanned by the robot;

[0007] When it is determined according to the first point cloud that the robot scans the target vehicle carrier, determining a second point cloud of each support leg according to the first point cloud;

[0008] Determining current position information of the target vehicle carrier according to the second point cloud of each support leg.

[0009] In an optional embodiment, the determining the second point cloud of each support leg according to the first point cloud includes:

[0010] Clustering the first point cloud to determine the point cloud of at least one support leg;

[0011] Determine whether the point clouds of at least one of the support legs contain all the support legs of the target vehicle. If not, determine the scanning position of the robot based on the determined point clouds of at least one of the support legs, and the robot can scan the point clouds of the support legs not included in the first point cloud at the scanning position;

[0012] Control the robot to move to the scanning position;

[0013] Control the robot to generate a third point cloud at the scanning position;

[0014] Cluster the third point cloud to determine the point clouds of at least one of the support legs;

[0015] Obtain the second point cloud of each support leg according to the point clouds of the support legs obtained by clustering the first point cloud and the point clouds of the support legs obtained by clustering the third point cloud.

[0016] In an alternative embodiment, the current position information includes: the current position coordinates of the target vehicle;

[0017] The determining the current position information of the target vehicle according to the second point cloud of each support leg includes:

[0018] For the second point cloud of each support leg, determine a target point that meets a first preset condition from the second point cloud, and determine the mean value of the coordinates of all points in the second point cloud on the first coordinate axis to obtain a first mean coordinate, where the first preset condition includes that the distance between the target point and the central axis of the target vehicle is the smallest in the second point cloud;

[0019] Determine the first target coordinates of all the target points on the second coordinate axis, where the second coordinate axis is perpendicular to the first coordinate axis;

[0020] Determine the mean value of all the first target coordinates to obtain a second mean coordinate, and determine the mean value of all the first mean coordinates to obtain a third mean coordinate;

[0021] Determine the current position coordinates of the target vehicle according to the second mean coordinate and the third mean coordinate.

[0022] In an alternative embodiment, the current position information includes: the movement vector corresponding to the target vehicle, the target vehicle includes four support legs, and two support legs are respectively arranged on both sides of the central axis;

[0023] The determining the current position information of the target vehicle according to the second point cloud of each support leg includes:

[0024] For each of the support legs, determine the calculation point corresponding to the current support leg. The coordinate of the calculation point corresponding to the current support leg on the first coordinate axis is the first mean coordinate corresponding to the current support leg, and the coordinate on the second coordinate axis is the first target coordinate corresponding to the current support leg;

[0025] For the two support legs on each side of the central axis, determine the target vector formed by the calculation points corresponding to the two support legs located on the same side of the central axis. The calculation point of the target vector close to the robot points to the calculation point away from the robot;

[0026] Determine whether the difference between the direction angles of the two target vectors is within a preset angle range. If so, use the target vector with the smaller direction angle among the two target vectors as the movement vector;

[0027] If not, use the mean of the coordinates of the two target vectors on the first coordinate axis as the coordinate of the movement vector on the first coordinate axis, and use the mean of the coordinates of the two target vectors on the second coordinate axis as the coordinate of the movement vector on the second coordinate axis to obtain the movement vector.

[0028] In an alternative embodiment, the determining that the robot scans the target vehicle according to the first point cloud includes:

[0029] Cluster all the points in the first point cloud according to the size information of the target vehicle to obtain a plurality of clustering results;

[0030] For every two of the plurality of clustering results, determine the first distance between the two clustering results;

[0031] When there is a first distance that satisfies a second preset condition among all the first distances, determine that the robot scans the target vehicle. The second preset condition includes: the first distance is within a preset distance range, and the preset distance range is set according to the size information of the target vehicle.

[0032] In an alternative embodiment, the clustering all the points in the first point cloud according to the size information of the target vehicle to obtain a plurality of clustering results includes:

[0033] For every two points in the first point cloud, determine the second distance between the two points;

[0034] Cluster all the points in the first point cloud according to the third preset condition and all the second distances to obtain a plurality of clustering results that meet the third preset condition. The third preset condition includes that the second distance between every two points in the clustering result is less than the first preset distance threshold, and the first preset distance threshold is set according to the size information of the target vehicle.

[0035] In an alternative embodiment, when there is an equal first distance among all the first distances that meets the second preset condition, determining that the robot scans the target vehicle includes:

[0036] When there is an equal first distance among all the first distances that meets the second preset condition, determine two target clustering results from the plurality of clustering results corresponding to the first point cloud, and the first distance between the two target clustering results meets the second preset condition;

[0037] Determine the third distance between each target clustering result and the preset initial position;

[0038] When both of the two third distances are less than or equal to the second preset distance threshold, determine that the robot scans the target vehicle.

[0039] In a second aspect, the present application provides a device for positioning a vehicle, including:

[0040] A control module, configured to control the robot to move towards the preset initial position of the target vehicle, and the target vehicle includes at least one support leg;

[0041] An acquisition module, configured to acquire the first point cloud scanned by the robot;

[0042] The determination module is further configured to, when determining that the robot scans the target vehicle according to the first point cloud, determine the second point cloud of each support leg according to the first point cloud;

[0043] The determination module is further configured to determine the current position information of the target vehicle according to the second point cloud of each support leg.

[0044] In a third aspect, the present application provides a robot, including: a processor and a memory, the processor is connected to the memory, and the processor is configured to execute the program for positioning a vehicle stored in the memory to implement the method for positioning a vehicle as described above.

[0045] In a fourth aspect, the present application further provides a storage medium, where the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method for positioning a vehicle as described above.

[0046] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art. The method provided by the embodiment of the present application includes: controlling the robot to move to a preset initial position of the target vehicle, where the target vehicle includes at least one support leg; obtaining the first point cloud scanned by the robot; when it is determined according to the first point cloud that the robot scans the target vehicle, determining the second point cloud of each support leg according to the first point cloud; and determining the current position information of the target vehicle according to the second point cloud of each support leg. In the above manner, during the process of controlling the robot to move to the preset initial position of the target vehicle, the embodiment of the present application obtains the first point cloud scanned by the robot. When it is determined according to the first point cloud that the robot scans the target vehicle, the second point cloud of each support leg of the target vehicle can be determined according to the obtained first point cloud, so as to determine the current position information of the target object according to the second point cloud of each support leg, thereby realizing the positioning of the target vehicle by the robot, avoiding setting a camera on the robot and a two-dimensional code on the target vehicle, and reducing the hardware cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

[0049] One or more embodiments are exemplarily illustrated by the pictures in the corresponding accompanying drawings. These exemplary illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements, unless otherwise stated, and the drawings in the drawings do not constitute a proportional limitation.

[0050] Figure 1 It is a schematic flowchart of a method for positioning a vehicle provided by an embodiment of the present application;

[0051] Figure 2 It is a schematic flowchart of another method for positioning a vehicle provided by an embodiment of the present application;

[0052] Figure 3 It is a schematic structural diagram of a device for positioning a vehicle provided by an embodiment of the present application;

[0053] Figure 4 It is a schematic structural diagram of a robot provided by an embodiment of the present application;

[0054] In the above drawings:

[0055] 10. Control module; 20. Acquisition module; 30. Determination module;

[0056] 400. Robot; 401. Processor; 402. Memory; 4021. Operating system; 4022. Application program; 403. User interface; 404. Network interface; 405. Bus system. Specific embodiments

[0057] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0058] The following disclosure provides many different embodiments or examples for implementing different structures of the present invention. To simplify the disclosure of the present invention, components and settings of specific examples are described below. Of course, they are only examples and are not intended to limit the present invention. In addition, the present invention may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplification and clarity and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0059] Referring to Figure 1 , Figure 1 is a schematic flowchart of a method for positioning a vehicle carrier provided by an embodiment of the present application. A method for positioning a vehicle carrier provided by an embodiment of the present application includes the following steps:

[0060] S101: Control the robot to move to a preset initial position of the target vehicle carrier, where the target vehicle carrier includes at least one support leg.

[0061] In this embodiment, the executing entity is a robot. The target vehicle is actually a cage car. The preset initial position is stored in a map or a server, or the robot is pushed to the parking position of the target vehicle, points are taken on the parking position of the target vehicle and saved on the map, so as to obtain the preset initial position of the target vehicle. The preset initial position is actually the position of the center point of the preset target vehicle. When the robot transports the target vehicle, the robot needs to move to the preset initial position, lift the target vehicle at the preset initial position, and transport the target vehicle. When the robot needs to transport the target vehicle, the preset initial position and the position of the robot are obtained. According to the position of the robot and the preset initial position, a planned path for the robot to move from the position of the robot to the preset initial position is determined, and the robot is controlled to move towards the preset initial position according to the planned path. However, since the target vehicle is placed by a person, there will be errors when placing the target vehicle, and there will also be errors when taking points on the parking position of the target vehicle through the map. If the robot directly moves to the preset initial position and lifts the target vehicle, it may affect the subsequent transportation of the target vehicle by the robot. Therefore, during the process of the robot moving from the current position to the preset initial position, it is necessary to determine the current position information of the target vehicle to achieve accurate positioning of the target vehicle by the robot, so that the robot can accurately lift the target vehicle.

[0062] S102: Obtain the first point cloud scanned by the robot.

[0063] In this embodiment, during the process of controlling the robot to move towards the preset initial position, laser points can be scanned through the lidar possessed by the robot itself. The laser points scanned by the lidar are by default based on the lidar as the coordinate system. In order to enable the robot to locate the position of the target vehicle according to the laser points scanned by the lidar, it is necessary to transform the laser points scanned by the lidar into the robot coordinate system, so as to obtain the first point cloud. Specifically, the robot in this embodiment itself has two lidars, one lidar is set in front of the robot, and one lidar is set behind the robot, so as to ensure that the robot has a 360-degree visual range. During the process of the robot moving towards the preset initial position, the laser points scanned are obtained through the two lidars respectively, the laser points scanned by the two lidars are transformed into the robot coordinate system, and all the points transformed by the two lidars into the robot coordinate system are fused into a point cloud, that is, the first point cloud in this embodiment. The first point cloud includes multiple points.

[0064] In the above, assume that A and B are respectively the coordinate transformation matrices of the two lidars to the robot coordinate system, C and D are respectively the point clouds based on their own coordinates collected by the lidars, and E is the fused point cloud (i.e., the first point cloud). The specific formula is as follows:

[0065] E = C×A + D×B

[0066] S103: When determining that the robot scans the target vehicle according to the first point cloud, determine the second point cloud of each support leg according to the first point cloud.

[0067] In this embodiment, after obtaining the first point cloud, it is necessary to use the first point cloud to determine whether the robot scans the target vehicle. When the robot scans the target vehicle, it indicates that the first point cloud scanned by the robot includes the point cloud of the support leg of the target vehicle. Therefore, by determining the second point cloud of each support leg of the target vehicle according to the first point cloud, the position of the target vehicle can be determined according to the second point cloud of each support leg, so as to realize the accurate positioning of the target vehicle.

[0068] S104: Determine the current position information of the target vehicle according to the second point cloud of each support leg.

[0069] In this embodiment, the current position information includes the current position coordinates of the target vehicle and the movement vector corresponding to the target vehicle. The current position coordinates are actually the destination where the robot needs to move. At the current position coordinates, the robot needs to lift the target vehicle to realize the handling of the target vehicle. After obtaining the current position coordinates and the movement vector corresponding to the target vehicle, the pose of the robot can be determined according to the current position coordinates and the target vehicle, so that the robot can move to the current position coordinates more accurately.

[0070] A method for positioning a vehicle provided in this embodiment, during the process of controlling the robot to move to the preset initial position of the target vehicle, obtain the first point cloud scanned by the robot. When determining that the robot scans the target vehicle according to the first point cloud, the second point cloud of each support leg of the target vehicle can be determined according to the obtained first point cloud, so as to determine the current position information of the target object according to the second point cloud of each support leg, thereby realizing the positioning of the target vehicle by the robot, avoiding setting a camera on the robot and a two-dimensional code on the target vehicle, and reducing the hardware cost.

[0071] Reference Figure 2 , Figure 2 is a schematic flowchart of another method for positioning a vehicle provided in an embodiment of the present application. A method for positioning a vehicle provided in an embodiment of the present application includes the following steps:

[0072] S201: Control the robot to move to the preset initial position of the target vehicle, and the target vehicle includes at least one support leg.

[0073] In this implementation, the step of S201 is the same as the step of S101 above. For details, reference can be made to the above description, and this embodiment will not be elaborated here.

[0074] S202: Obtain the first point cloud scanned by the robot.

[0075] In this embodiment, during the process of the robot moving to the preset initial position, scanning can be performed according to the preset initial position and the preset error coefficient to obtain the first point cloud scanned by the robot. The preset error coefficient can be set according to actual needs, and the specific value of the preset error coefficient is not limited in this embodiment. According to the preset initial position and the size information of the target vehicle stored in advance, the scanning range required by the robot can be determined. However, since there may be errors in the preset initial position, the preset error coefficient is set to expand the scanning range of the robot, so as to obtain the first point cloud scanned by the robot. By setting the preset error coefficient, the error brought by subsequent positioning of the target vehicle is reduced, and the accuracy of the robot's positioning of the target vehicle is improved.

[0076] S203: When it is determined according to the first point cloud that the robot scans the target vehicle, cluster the first point cloud to determine the point clouds of at least one support leg.

[0077] In this embodiment, in step S203, determining that the robot scans the target vehicle according to the first point cloud includes:

[0078] Cluster all the points in the first point cloud according to the size information of the target vehicle to obtain multiple clustering results;

[0079] For every two clustering results among the multiple clustering results, determine the first distance between the two clustering results;

[0080] When there is a first distance that satisfies the second preset condition among all the first distances, it is determined that the robot scans the target vehicle. The second preset condition includes: the first distance is within the preset distance range, and the preset distance range is set according to the size information of the target vehicle.

[0081] Among them, the size information of the target vehicle specifically refers to the leg width of each support leg of the target vehicle and the distance between the support legs. After obtaining the first point cloud, based on the leg width of the support legs of the target vehicle, all the points in the first point cloud can be clustered to cluster the points in the first point cloud that are similar to the support legs of the target vehicle, thereby obtaining multiple clustering results. Although the clustering method can cluster all the points in the first point cloud that are similar to the support legs of the target vehicle, it is inevitable that some points similar to the support legs of the target vehicle, such as tables and chairs, will be clustered in the obtained clustering results. This will affect the accuracy of the robot's positioning of the target vehicle. Therefore, in order to accurately determine the clustering result corresponding to the support legs of the target vehicle from multiple clustering results, a preset distance range can be set according to the distance between the support legs of the target vehicle, and the first distance between every two clustering results can be determined to obtain all the first distances. When there is a first distance equal to the first distance within the preset distance range among all the first distances, it is determined that the matching with the target vehicle is successful, and further it is determined that the robot scans the target vehicle.

[0082] Specifically, a third preset distance threshold and a fourth preset distance threshold can be preset. Determine the sum value between the distance between the support legs and the third preset distance threshold, and determine the difference value between the distance between the support legs and the fourth preset distance threshold. The range between the difference value and the sum value is used as the preset distance range. Among them, the third preset distance threshold and the fourth preset distance threshold can be the same or different. The specific values of the third preset distance threshold and the fourth preset distance threshold can be set according to actual needs, and are not elaborated here in this embodiment. The first distance between two clustering results specifically refers to the first distance between the clustering centers of the two clustering results. The determination of the clustering center can be carried out according to the existing method, and is not elaborated here in this embodiment.

[0083] In the above, in order to further improve the accuracy of the robot's positioning of the target vehicle, when there is a first distance satisfying the second preset condition among all the first distances, it is determined that the robot scans the target vehicle, which specifically includes:

[0084] When there is a first distance satisfying the second preset condition among all the first distances, two target clustering results are determined from the multiple clustering results corresponding to the first point cloud, and the first distance between the two target clustering results satisfies the second preset condition;

[0085] Determine the third distance between each target clustering result and the preset initial position;

[0086] When both of the two third distances are less than or equal to the second preset distance threshold, it is determined that the robot scans the target vehicle.

[0087] In this embodiment, during the process of the robot moving to the preset initial position, if the clustering result corresponding to the support leg of the target vehicle determined is far from the preset initial position, and the above clustering result is still used as the support leg of the target vehicle, it will affect the accuracy of determining that the robot scans the target vehicle. Therefore, when there is a first distance that meets the second preset condition among all the first distances, in this embodiment, two target clustering results corresponding to the first distance that meets the second preset condition are determined from the multiple clustering results corresponding to the first point cloud, and the third distance between each target clustering result and the preset initial position is determined. When both of the two third distances are less than or equal to the second preset distance threshold, it indicates that the obtained clustering result corresponding to the support leg of the target vehicle is close to the preset initial position, and the above clustering result can be used as the support leg of the target vehicle. Among them, the third distance between the target clustering result and the preset initial position specifically refers to the third distance between the clustering center of the target clustering result and the preset initial position. The determination of the clustering center of the target clustering result can be achieved according to the existing technology, and this is not elaborated in this embodiment. The second preset distance threshold can be set according to actual needs, and the specific value of the second preset distance threshold is not limited in this embodiment.

[0088] It should be noted that when there are at least two first distances that meet the second preset condition among all the first distances, the target clustering results corresponding to each first distance are determined from the multiple clustering results corresponding to the first point cloud, and the third distance between each target clustering result and the preset initial position is determined. Since the robot may first scan the front two support legs of the target vehicle during the movement towards the target vehicle, if there are two third distances among all the obtained third distances that are less than or equal to the second preset distance threshold, it can be determined that the robot scans the target vehicle. When it is determined that the robot scans the target vehicle, all the target clustering results can be determined from the multiple clustering results of the first point cloud pair, where the first distance between every two target clustering results among all the target clustering results meets the second preset condition, and the point cloud corresponding to each target clustering result is determined as the point cloud of the support leg.

[0089] As mentioned above, all the points in the first point cloud are clustered according to the size information of the target vehicle to obtain multiple clustering results, including:

[0090] For every two points in the first point cloud, the second distance between the two points is determined;

[0091] All the points in the first point cloud are clustered according to the third preset condition and all the second distances to obtain multiple clustering results that meet the third preset condition. The third preset condition includes that the second distance between every two points in the clustering result is less than the first preset distance threshold, and the first preset distance threshold is set according to the size information of the target vehicle.

[0092] In this embodiment, each support leg of the target vehicle has an inherent characteristic, that is, the leg width of the support leg is a certain value. Therefore, after obtaining the first point cloud, for every two points in the same plane coordinate system, determine the second distance between every two points in the same plane coordinate system, and cluster all the points whose second distance is less than the first preset distance threshold, so as to obtain all the clustering results. The second distance being less than the first preset distance threshold indicates that the two points belong to the same support leg. In this way, all the points belonging to the same support leg can be clustered. The first preset distance threshold is actually the leg width of the support leg.

[0093] Regarding the step of clustering the first point cloud and determining the point cloud of at least one support leg in the above S203 step, since it is determined that the robot scans the target vehicle based on whether the first point cloud includes the point cloud of the support leg of the target vehicle. When it is determined that the robot scans the target vehicle, all target clustering results whose first distance meets the second preset condition can be determined from the multiple clustering results corresponding to the first point cloud, and all the points in each target clustering result are determined as the point cloud of a support leg in the target vehicle, so as to obtain the point cloud of at least one support leg.

[0094] S204: Determine whether the point cloud of at least one support leg contains all the support legs of the target vehicle.

[0095] S205: If not, determine the scanning position of the robot according to the determined point cloud of at least one support leg, and the robot can scan the point cloud of the support leg not included in the first point cloud at the scanning position.

[0096] Regarding the above S204 step and S205 step, the first point cloud may include the point cloud of all the support legs of the target vehicle. Of course, since the robot moves towards the target vehicle, the first point cloud may not include the point cloud of all the support legs of the target vehicle, and the first point cloud only includes the point cloud of the front support legs of the target vehicle. Therefore, after determining the point cloud of at least one support leg from the first point cloud, if the point cloud of at least one support leg does not contain all the support legs of the target vehicle, all target clustering results that meet the second preset condition can be determined from the multiple clustering results corresponding to the first point cloud, and calculations are performed by combining the clustering centers of all the target clustering results and the pre-stored size information of the target vehicle with the pre-established mathematical model, so as to determine the scanning position of the robot. This scanning position is actually a position near the center point of the target vehicle. Since the scanning position is not determined by the point cloud of all the support legs of the target vehicle, there may still be errors when the robot jacks up the target vehicle at the scanning position. Therefore, the following S206 step to S209 step need to be executed to obtain the point cloud of all the support legs of the target vehicle to achieve accurate positioning of the target vehicle.

[0097] S206: Control the robot to move to the scanning position.

[0098] S207: Control the robot to generate a third point cloud at the scanning position.

[0099] S208: Cluster the third point cloud to determine the point clouds of at least one support leg.

[0100] S209: Obtain the second point cloud of each support leg according to the point clouds of the support legs obtained by clustering the first point cloud and the point clouds of the support legs obtained by clustering the third point cloud.

[0101] Regarding the above steps S206 to S209, since the robot can scan the point clouds of all the support legs of the target vehicle when at the scanning position, after obtaining the scanning position of the robot, control the robot to move to the scanning position. After the robot moves to the scanning position, the robot scans to obtain a third point cloud, and cluster the third point cloud to obtain the point clouds of the support legs not included in the first point cloud. Thus, according to the point clouds of the support legs obtained by clustering the first point cloud and the point clouds of the support legs obtained by clustering the third point cloud, obtain the second point cloud of each support leg. The way of clustering the third point cloud is similar to the way of clustering the first point cloud, and it is also realized by clustering according to the characteristics of the support legs of the target vehicle. For details, refer to the above way of clustering the first point cloud, which will not be elaborated in this embodiment. When the robot moves to the scanning position to obtain the third point cloud, it is necessary to reduce the preset error coefficient to reduce the scanning interference of the robot and improve the accuracy of the robot scanning each support leg of the target vehicle. The reduced preset error coefficient can be determined according to the mileage from the position where the robot determines that it has scanned the target vehicle to the scanning position.

[0102] Among them, when determining the point clouds of the support legs not included in the first point cloud, for each two clustering results among all the clustering results obtained by clustering the third point cloud, determine the first distance between the two clustering results. Determine all the target clustering results corresponding to the first distances whose first distances are within the preset distance range from the third point cloud. Determine the clustering results corresponding to the support legs not included in the first point cloud from all the target clustering results, and determine all the points in the clustering results corresponding to the support legs not included in the first point cloud as the point clouds of the support legs not included in the first point cloud. Thus, according to the point clouds of the support legs obtained by clustering the first point cloud and the point clouds of the support legs obtained by clustering the third point cloud, obtain the second point clouds of each support leg of the target vehicle. The preset distance range is the same as the above, for details, refer to the above, which will not be elaborated in this embodiment.

[0103] S210: If so, determine the point cloud of each support leg in the first point cloud as the second point cloud.

[0104] In this embodiment, if all the support legs of all target vehicles are already included in the first point cloud, therefore, based on the point clouds of all the support legs of all target vehicles, the target vehicles can be accurately located. Thus, the point cloud of each support leg in the first point cloud can be determined as the second point cloud for subsequent accurate positioning of the target vehicles.

[0105] S211: Determine the current position information of the target vehicle according to the second point cloud of each support leg.

[0106] In this embodiment, in step S211, determining the current position information of the target vehicle according to the second point cloud of each support leg includes:

[0107] For the second point cloud of each support leg, determine the target points that meet the first preset condition from the second point cloud, and determine the mean value of the coordinates of all points in the second point cloud on the first coordinate axis to obtain the first mean coordinate. The first preset condition includes that, in the second point cloud, the distance between the target point and the central axis of the target vehicle is the smallest;

[0108] Determine the first target coordinates of all target points on the second coordinate axis, where the second coordinate axis is perpendicular to the first coordinate axis;

[0109] Determine the mean value of all the first target coordinates to obtain the second mean coordinate, and determine the mean value of all the first mean coordinates to obtain the third mean coordinate;

[0110] Determine the current position coordinates of the target vehicle according to the second mean coordinate and the third mean coordinate.

[0111] Among them, the current position information includes the current position coordinates of the target vehicle. The first coordinate axis is the central axis of the target vehicle, and the included angle between the first coordinate axis and the moving direction of the robot is less than the included angle between the second coordinate axis and the moving direction of the robot. The first coordinate axis is actually the X-axis, and the second coordinate axis is actually the Y-axis. In this embodiment, for the convenience of calculation, it is assumed that the robot moves as close as possible to the central axis of the target vehicle. When determining the coordinates of each support leg, determine the target points with the smallest distance from the central axis of the target vehicle from the second point clouds corresponding to the respective support legs, and use the first target coordinates of the target points on the second coordinate axis as the coordinates of the support legs on the Y-axis. Determine the mean value of the coordinates of all points in the second point clouds corresponding to each support leg on the first coordinate axis to obtain the first mean coordinate, and use the first mean coordinate as the coordinates of the support legs on the X-axis. Calculate the mean value of the coordinates of all support legs on the X-axis to obtain the second mean coordinate, calculate the mean value of the coordinates of all support legs on the Y-axis to obtain the third mean coordinate, determine the XY-axis coordinates corresponding to the target vehicle from the second mean coordinate and the third mean coordinate, and determine the XY-axis coordinates corresponding to the target vehicle as the current position coordinates of the target vehicle.

[0112] In this embodiment, the current position information further includes a movement vector corresponding to the target vehicle. The target vehicle includes four support legs, and two support legs are respectively arranged on both sides of the central axis of the target vehicle. In order to further improve the positioning accuracy of the robot for the target vehicle, in the above, determining the current position information of the target vehicle according to the second point cloud of each support leg includes:

[0113] For each support leg, determining a calculation point corresponding to the current support leg. The coordinate of the calculation point corresponding to the current support leg on the first coordinate axis is the first mean coordinate corresponding to the current support leg, and the coordinate on the second coordinate axis is the first target coordinate corresponding to the current support leg;

[0114] For the two support legs on each side of the central axis, determining a target vector formed by the calculation points corresponding to the two support legs located on the same side of the central axis. The calculation point of the target vector close to the robot points to the calculation point away from the robot;

[0115] Judging whether the difference between the direction angles of the two target vectors is within a preset angle range. If so, taking the target vector with the smaller direction angle among the two target vectors as the movement vector;

[0116] If not, taking the mean of the coordinates of the two target vectors on the first coordinate axis as the coordinate of the movement vector on the first coordinate axis, and taking the mean of the coordinates of the two target vectors on the second coordinate axis as the coordinate of the movement vector on the second coordinate axis to obtain the movement vector.

[0117] Specifically, after obtaining the first mean coordinates (coordinates on the X-axis) and the first target coordinates (coordinates on the Y-axis) of each support leg, the calculation points corresponding to the support legs are determined by the coordinates of each support leg on the X-axis and the coordinates on the Y-axis. For the two support legs on each side of the central axis, the direction from the calculation point of the support leg close to the robot to the calculation point of the support leg far from the robot is used to form a target vector, so that two target vectors can be obtained. The direction angle of the target vector can be determined according to the existing method for determining the direction angle of a vector, so as to obtain the determination of the direction angles of the two target vectors. Determine the difference between the direction angles of the two target vectors. When the difference is within the preset angle range, it indicates that the direction angles of the two target vectors are close, and the target vector with the smaller direction angle among the target vectors is used as the movement vector. When the difference is not within the preset angle range, it indicates that the direction angles of the two target vectors are quite different. At this time, the mean value of the coordinates of the two target vectors on the first coordinate axis is used as the coordinate of the movement vector on the first coordinate axis, and the mean value of the coordinates of the two target vectors on the second coordinate axis is used as the coordinate of the movement vector on the second coordinate axis. According to the coordinates of the movement vector on the first coordinate axis and the coordinates on the second coordinate axis, the movement vector is obtained, and the direction of the movement vector is the same as the direction of each target vector. After obtaining the movement vector, the pose of the robot can be determined according to the movement vector and the current position coordinates, so that the robot can accurately move to the current position coordinates, thereby realizing the handling of the target vehicle at the current position coordinates.

[0118] A method for positioning a vehicle provided in this embodiment, during the process of controlling the robot to move to the preset initial position of the target vehicle, the first point cloud scanned by the robot is obtained. When it is determined that the robot scans the target vehicle according to the first point cloud, the second point cloud of each support leg of the target vehicle can be determined according to the obtained first point cloud, so as to determine the current position information of the target object according to the second point cloud of each support leg, thereby realizing the positioning of the target vehicle by the robot, avoiding setting a camera on the robot and a two-dimensional code on the target vehicle, and reducing the hardware cost.

[0119] Reference Figure 3 , Figure 3A schematic structural diagram of a device for positioning a vehicle provided by an embodiment of the present application. A device for positioning a vehicle provided by this embodiment includes: a control module 10, an acquisition module 20, and a determination module 30. Among them, the control module is used to control the robot to move to a preset initial position of the target vehicle, and the target vehicle includes at least one support leg; the acquisition module is used to acquire the first point cloud scanned by the robot; the determination module is further used to, when determining that the robot scans the target vehicle according to the first point cloud, determine the second point cloud of each support leg according to the first point cloud; the determination module is further used to determine the current position information of the target vehicle according to the second point cloud of each support leg.

[0120] In this embodiment, the determination module 20 is further used to:

[0121] Cluster the first point cloud to determine the point cloud of at least one support leg;

[0122] Judge whether the point cloud of at least one support leg contains all the support legs of the target vehicle. If not, determine the scanning position of the robot according to the determined point cloud of at least one support leg, and the robot can scan the point cloud of the support leg not included in the first point cloud at the scanning position;

[0123] Control the robot to move to the scanning position;

[0124] Control the robot to generate a third point cloud at the scanning position;

[0125] Cluster the third point cloud to determine the point cloud of at least one support leg;

[0126] Obtain the second point cloud of each support leg according to the point cloud of the support leg obtained by clustering the first point cloud and the point cloud of the support leg obtained by clustering the third point cloud.

[0127] In this embodiment, the current position information includes: the current position coordinates of the target vehicle.

[0128] In this embodiment, the determination module 30 is further used to:

[0129] For the second point cloud of each support leg, determine a target point that meets the first preset condition from the second point cloud, and determine the mean value of the coordinates of all points in the second point cloud on the first coordinate axis to obtain the first mean coordinate. The first preset condition includes that in the second point cloud, the distance between the target point and the central axis of the target vehicle is the smallest;

[0130] Determine the first target coordinates of all the target points on the second coordinate axis, where the second coordinate axis is perpendicular to the first coordinate axis;

[0131] Determine the mean value of all the first target coordinates to obtain the second mean coordinate, and determine the mean value of all the first mean coordinates to obtain the third mean coordinate;

[0132] Determine the current position coordinates of the target vehicle according to the second mean coordinate and the third mean coordinate.

[0133] In this embodiment, the current position information includes: the movement vector corresponding to the target vehicle, the target vehicle includes four support legs, and two support legs are respectively arranged on both sides of the central axis.

[0134] In this embodiment, the determination module 30 is further configured to:

[0135] For each support leg, determine the calculation point corresponding to the current support leg, where the coordinate of the calculation point corresponding to the current support leg on the first coordinate axis is the first mean coordinate corresponding to the current support leg, and the coordinate on the second coordinate axis is the first target coordinate corresponding to the current support leg;

[0136] For the two support legs on each side of the central axis, determine the target vector formed by the calculation points corresponding to the two support legs located on the same side of the central axis, and the target vector points from the calculation point close to the robot to the calculation point far from the robot;

[0137] Judge whether the difference between the direction angles of the two target vectors is within a preset angle range. If so, use the target vector with the smaller direction angle among the two target vectors as the movement vector;

[0138] If not, use the mean value of the coordinates of the two target vectors on the first coordinate axis as the coordinate of the movement vector on the first coordinate axis, and use the mean value of the coordinates of the two target vectors on the second coordinate axis as the coordinate of the movement vector on the second coordinate axis to obtain the movement vector.

[0139] In this embodiment, the determination module 30 is further configured to:

[0140] Cluster all the points in the first point cloud according to the size information of the target vehicle to obtain a plurality of clustering results;

[0141] For every two of the plurality of clustering results, determine the first distance between the two clustering results;

[0142] When there is a first distance that meets the second preset condition among all the first distances, it is determined that the robot scans the target vehicle. The second preset condition includes that the first distance is within a preset distance range, and the preset distance range is set according to the size information of the target vehicle.

[0143] In this embodiment, the determination module 30 is further configured to:

[0144] For every two points in the first point cloud, determine the second distance between the two points;

[0145] According to the third preset condition and all the second distances, cluster all the points in the first point cloud to obtain multiple clustering results that meet the third preset condition. The third preset condition includes that the second distance between every two points in the clustering result is less than a first preset distance threshold, and the first preset distance threshold is set according to the size information of the target vehicle.

[0146] In this embodiment, the determination module 30 is further configured to:

[0147] When there is a first distance among all the first distances that is equal to the first distance that meets the second preset condition, determine two target clustering results from the multiple clustering results corresponding to the first point cloud, and the first distance between the two target clustering results meets the second preset condition;

[0148] Determine the third distance between each target clustering result and the preset initial position;

[0149] When both of the two third distances are less than or equal to a second preset distance threshold, it is determined that the robot scans the target vehicle.

[0150] A device for positioning a vehicle provided in this embodiment, during the process of controlling the robot to move towards the preset initial position of the target vehicle, obtains the first point cloud scanned by the robot. When it is determined according to the first point cloud that the robot scans the target vehicle, the second point cloud of each support leg of the target vehicle can be determined according to the obtained first point cloud, so as to determine the current position information of the target object according to the second point cloud of each support leg, thereby realizing the positioning of the target vehicle by the robot, avoiding setting a camera on the robot and a two-dimensional code on the target vehicle, and reducing the hardware cost.

[0151] Figure 4 It is a schematic structural diagram of a robot provided in an embodiment of the present application. Figure 4The shown robot 400 includes: at least one processor 401, a memory 402, at least one network interface 404, and other user interfaces 403. Each component in the robot 400 is coupled together through a bus system 405. It can be understood that the bus system 405 is used to implement the connection and communication between these components. In addition to a data bus, the bus system 405 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clear illustration, in Figure 4 all kinds of buses are labeled as the bus system 405.

[0152] Among them, the user interface 403 may include a display, a keyboard, or a pointing device (for example, a mouse, a trackball, a touchpad, or a touch screen, etc.).

[0153] It can be understood that the memory 402 in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). The memory 402 described herein is intended to include but not be limited to these and any other suitable types of memory.

[0154] In some embodiments, the memory 402 stores the following elements, executable units, or data structures, or subsets thereof, or extended sets thereof: an operating system 4021 and an application program 4022.

[0155] Among them, the operating system 4021 includes various system programs, such as the framework layer, the core library layer, the driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program 4022 includes various application programs, such as a Media Player, a Browser, etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present application may be included in the application program 4022.

[0156] In the embodiment of the present application, by calling the program or instruction stored in the memory 402, specifically, it may be the program or instruction stored in the application program 4022, the processor 401 is used to execute the method steps provided by each method embodiment, for example, including: controlling the robot to move to a preset initial position of the target vehicle, where the target vehicle includes at least one support leg; obtaining the first point cloud scanned by the robot; when it is determined according to the first point cloud that the robot scans the target vehicle, determining the second point cloud of each support leg according to the first point cloud; and determining the current position information of the target vehicle according to the second point cloud of each support leg.

[0157] The method disclosed in the above embodiment of the present application can be applied to the processor 401 or implemented by the processor 401. The processor 401 may be an integrated circuit chip with signal processing capabilities. During the implementation process, each step of the above method may be completed by the integrated logic circuit in the hardware of the processor 401 or by the instruction in the form of software. The above-mentioned processor 401 may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiment of the present application may be directly embodied as being executed by the hardware decoding processor or completed by the combination of the hardware and software units in the decoding processor. The software unit may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 402, and the processor 401 reads the information in the memory 402 and combines its hardware to complete the steps of the above method.

[0158] It will be understood that the embodiments described herein may be implemented using hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the processing unit may be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application, or any combination thereof.

[0159] For a software implementation, the techniques described herein may be implemented by units that execute the functions described herein. The software code may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.

[0160] The robot provided in this embodiment may be a robot as shown in Figure 4 and may execute all steps of the method for positioning a vehicle as shown in Figure 1 and Figure 2 so as to achieve the technical effects of the method for positioning a vehicle as shown in Figure 1 and Figure 2 For specific details, please refer to the relevant descriptions in Figure 1 and Figure 2 For the sake of brevity, no further details will be provided here.

[0161] The embodiments of the present application further provide a storage medium (computer-readable storage medium). The storage medium stores one or more programs. Among them, the storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; the memory may also include a combination of the above types of memory.

[0162] When one or more programs in the storage medium can be executed by one or more processors, the method for positioning a vehicle executed on the device side of the vehicle positioning device can be implemented.

[0163] The processor is used to execute a program for positioning a vehicle stored in a memory to implement the following steps of a method for positioning a vehicle executed on the device side of the positioning vehicle: controlling a robot to move towards a preset initial position of a target vehicle, the target vehicle including at least one support leg; acquiring a first point cloud scanned by the robot; when it is determined according to the first point cloud that the robot scans the target vehicle, determining a second point cloud of each support leg according to the first point cloud; and determining current position information of the target vehicle according to the second point cloud of each support leg.

[0164] Those skilled in the art should also be able to further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0165] It should be noted that the phrases such as "one embodiment", "embodiment", "exemplary embodiment", "some embodiments", etc. mentioned in the specification indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment includes such specific features, structures, or characteristics. In addition, such phrases do not necessarily refer to the same embodiment. Moreover, when combining specific features, structures, or characteristics with an embodiment, it is within the knowledge scope of those skilled in the art to implement such features, structures, or characteristics in combination with other embodiments, whether explicitly or implicitly described.

[0166] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article, or device including the said element.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for positioning a vehicle, characterized in that, Applied to a robot, the method includes: Controlling the robot to move to a preset initial position of a target vehicle, where the target vehicle includes at least one support leg; Obtaining a first point cloud scanned by the robot; When it is determined according to the first point cloud that the robot scans the target vehicle, determining a second point cloud of each support leg according to the first point cloud; Determining current position information of the target vehicle according to the second point cloud of each support leg.

2. The method according to claim 1, wherein The determining the second point cloud of each support leg according to the first point cloud includes: Clustering the first point cloud to determine the point cloud of at least one support leg; Judging whether the point cloud of at least one support leg contains all the support legs of the target vehicle. If not, according to the determined point cloud of at least one support leg, determining the scanning position of the robot, where the robot can scan the point cloud of the support leg not included in the first point cloud at the scanning position; Controlling the robot to move to the scanning position; Controlling the robot to generate a third point cloud at the scanning position; Clustering the third point cloud to determine the point cloud of at least one support leg; Obtaining the second point cloud of each support leg according to the point cloud of the support leg obtained by clustering the first point cloud and the point cloud of the support leg obtained by clustering the third point cloud.

3. The method according to claim 1, wherein The current position information includes: the current position coordinates of the target vehicle; The determining the current position information of the target vehicle according to the second point cloud of each support leg includes: For the second point cloud of each support leg, determining a target point that meets a first preset condition from the second point cloud, and determining the mean value of the coordinates of all points in the second point cloud on a first coordinate axis to obtain a first mean coordinate, where the first preset condition includes that in the second point cloud, the distance between the target point and the central axis of the target vehicle is the smallest; Determining a first target coordinate of all the target points on a second coordinate axis, where the second coordinate axis is perpendicular to the first coordinate axis; Determining the mean value of all the first target coordinates to obtain a second mean coordinate, and determining the mean value of all the first mean coordinates to obtain a third mean coordinate; Determining the current position coordinates of the target vehicle according to the second mean coordinate and the third mean coordinate.

4. The method according to claim 3, wherein The current position information includes: a movement vector corresponding to the target vehicle. The target vehicle includes four support legs, and two support legs are arranged on each side of the central axis; The determining the current position information of the target vehicle according to the second point cloud of each support leg includes: For each support leg, determining a calculation point corresponding to the current support leg, where the coordinate of the calculation point corresponding to the current support leg on the first coordinate axis is the first mean coordinate corresponding to the current support leg, and the coordinate on the second coordinate axis is the first target coordinate corresponding to the current support leg; For two of the support legs on each side of the central axis, determine a target vector formed by the calculation points corresponding to the two support legs on the same side of the central axis, where the calculation point of the target vector close to the robot points to the calculation point away from the robot; Determine whether the difference between the direction angles of the two target vectors is within a preset angle range. If so, use the target vector with the smaller direction angle among the two target vectors as the movement vector; If not, use the mean value of the coordinates of the two target vectors on the first coordinate axis as the coordinate of the movement vector on the first coordinate axis, and use the mean value of the coordinates of the two target vectors on the second coordinate axis as the coordinate of the movement vector on the second coordinate axis to obtain the movement vector.

5. The method according to claim 1, characterized in that The determining that the robot scans the target vehicle according to the first point cloud includes: Cluster all the points in the first point cloud according to the size information of the target vehicle to obtain a plurality of clustering results; For every two of the plurality of clustering results, determine a first distance between the two clustering results; When there is a first distance that satisfies a second preset condition among all the first distances, determine that the robot scans the target vehicle. The second preset condition includes: the first distance is within a preset distance range, and the preset distance range is set according to the size information of the target vehicle.

6. The method according to claim 5, wherein The clustering all the points in the first point cloud according to the size information of the target vehicle to obtain a plurality of clustering results includes: For every two points in the first point cloud, determine a second distance between the two points; Cluster all the points in the first point cloud according to a third preset condition and all the second distances to obtain a plurality of clustering results that satisfy the third preset condition. The third preset condition includes that the second distance between every two points in the clustering result is less than a first preset distance threshold, and the first preset distance threshold is set according to the size information of the target vehicle.

7. The method according to claim 5, characterized in that, The determining that the robot scans the target vehicle when there is a first distance equal to the first distance that satisfies the second preset condition among all the first distances includes: When there is a first distance equal to the first distance that satisfies the second preset condition among all the first distances, determine two target clustering results from the plurality of clustering results corresponding to the first point cloud, and the first distance between the two target clustering results satisfies the second preset condition; Determine a third distance between each target clustering result and the preset initial position; When both of the two third distances are less than or equal to a second preset distance threshold, determine that the robot scans the target vehicle.

8. A device for positioning a vehicle, characterized in that, Includes: A control module for controlling the robot to move to a preset initial position of the target vehicle, where the target vehicle includes at least one support leg; An acquisition module for acquiring a first point cloud scanned by the robot; The determining module is further configured to, when determining that the robot scans the target vehicle according to the first point cloud, determine a second point cloud of each support leg according to the first point cloud; The determining module is further configured to determine the current position information of the target vehicle according to the second point cloud of each of the support legs.

9. A robot, characterized in that, It includes: A processor and a memory, the processor is connected to the memory, and the processor is configured to execute a program for positioning a vehicle stored in the memory to implement the method for positioning a vehicle according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method for positioning a vehicle according to any one of claims 1 to 7.

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