Robot relocation method, device and robot based on two-dimensional grid map

Abstract

The present invention provides a robot repositioning method based on a two-dimensional grid map, S1, obtaining a robot repositioning instruction; S2, obtaining a laser point cloud frame obtained by the robot; S3, converting the laser point cloud frame into a two-dimensional grid sub-image; S4, dividing the two-dimensional scene grid map into multiple grid atlases; S5, performing image feature vectorization on the two-dimensional grid sub-image to obtain a first feature vector; S6, performing image feature vectorization on all sub-images of the multiple grid atlases to obtain a second feature vector set; S7, calculating the vector two norm of the second feature vector set and the first feature vector to obtain a small grid map that minimizes the difference between the vector two norms; S8, loading the center path track point corresponding to the small grid map, and using this point as the robot initial point. The present invention divides a large indoor grid map into multiple small grid maps with different widths and heights with the track point as the center, and it is easier to find similar grids based on image information, thereby realizing robot repositioning better and faster.

Description

Technical Field

[0001] The present invention relates to the field of robot technology, and in particular to a robot repositioning method, device and robot based on a two-dimensional grid map. Background Art

[0002] Autonomous navigation mobile robots require that the mobile robot can achieve point-to-point autonomous path-finding walking capabilities. The premise for achieving this capability is that the mobile robot knows its current location and the location of the target point to be reached. Therefore, the positioning technology of autonomous navigation mobile robots has been one of the hot research technologies in recent years. Currently, the positioning methods widely used in indoor autonomous navigation mobile robots include laser positioning, QR code positioning, UWB positioning, and visual positioning. Among them, the laser positioning method has become the preferred positioning method for indoor autonomous mobile robots due to its high positioning accuracy, mature technical solutions, reasonable price, and easy installation.

[0003] refer to Figure 1 The positioning process of the laser positioning method is mainly as follows: first, prepare a two-dimensional grid map of the indoor contour in advance. The grid map is also generated by inputting laser scanning frames, manually controlling the robot to walk in the scene, and using SLAM technology to generate a navigation map (the grid map has three colors, the default gray represents the place where the robot does not need to go, and the white represents the place where the robot potentially needs to walk, and the black represents the two-dimensional contour shape of the room). Secondly, it is necessary to manually give the approximate location of the robot in the map, initialize the position of the robot, and then realize the robot's autonomous walking positioning and navigation function. Therefore, indoor patrol robots must face a headache, that is, once the robot's position is lost, it is necessary to manually set the initial position of the robot, which affects the robot's long-term unmanned operation.

[0004] At present, there are two main technical methods to solve the problem of position loss of indoor autonomous mobile robots:

[0005] 1. Introducing auxiliary equipment: For example, in relatively open areas, GPS can be used for relocation. In closed rooms, UWB equipment can be installed in advance for global relocation. Cameras can be used in places where image features are obvious. The use of auxiliary equipment, especially for indoor positioning, requires the deployment of UWB equipment in advance when using UWB, which will increase costs because GPS is not accurate in indoor positioning.

[0006] 2. Do not introduce auxiliary equipment and use a repositioning algorithm: for example, use the global traversal method, that is, randomly give an initial position, and then start selecting values ​​at a fixed step size to find a position that makes the current laser scanning frame most suitable for the map. This solution can easily fall into the wrong position.

[0007] The background description provided herein is for the purpose of generally presenting the context of the present disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims of this application and are not admitted to be prior art by inclusion in this section. Summary of the invention

[0008] In view of the above technical problems in the related art, the present invention proposes a robot relocalization method based on a two-dimensional grid map, which comprises the following steps:

[0009] S1, obtain the robot relocation instruction and load the robot path trajectory points saved during map building;

[0010] S2, obtain the laser point cloud frame acquired by the robot;

[0011] S3, converting the laser point cloud frame into a two-dimensional grid sub-map cur_map;

[0012] S4, dividing the two-dimensional scene grid map into a plurality of grid atlases sub_maps with a width and height of M*M with each path trajectory point as the center;

[0013] S5, performing image feature vectorization on the two-dimensional grid sub-image cur_map to obtain a first feature vector cur_d;

[0014] S6, performing image feature vectorization on all sub-maps of the plurality of raster atlases sub_maps to obtain a second feature vector set d;

[0015] S7, calculating the vector second norm of the second feature vector set and the first feature vector to obtain a grid mini-map in a plurality of grid atlases that minimizes the difference between the vector second norms;

[0016] S8, loading the center path trajectory point corresponding to the small grid map, and using this point as the robot's initial point.

[0017] Specifically, step S2 includes: controlling the robot to rotate in situ to obtain a 360-degree laser point cloud frame.

[0018] Specifically, step S2 specifically includes: using the IMU rotation angle yaw information to merge and splice the scanning frames scan of the laser radar during the rotation process into a laser point cloud frame S.

[0019] Specifically, in step S5, the image feature vectorization is performed on the two-dimensional grid sub-image cur_map, and in S6, the image feature vectorization is performed on the two-dimensional grid sub-image cur_map, both of which use DBOW3 for image feature vectorization.

[0020] Specifically, the feature vectors in the first feature vector and the second feature vector set are both 64-dimensional.

[0021] In the second aspect, another aspect of the present invention discloses a robot repositioning device based on a two-dimensional grid map, which includes the following units:

[0022] The repositioning instruction acquisition unit is used to obtain the robot repositioning instruction and load the robot path trajectory points saved during map building;

[0023] A laser point cloud frame acquisition unit, used to acquire the laser point cloud frame acquired by the robot;

[0024] A two-dimensional grid sub-image acquisition unit, used for converting the laser point cloud frame into a two-dimensional grid sub-image;

[0025] A sub-grid map acquisition unit, used to divide the two-dimensional scene grid map into a plurality of grid atlases with a width and height of M*M with each path trajectory point as the center;

[0026] A two-dimensional grid sub-image feature acquisition unit, used for performing image feature vectorization on the two-dimensional grid sub-image to obtain a first feature vector;

[0027] A sub-grid map feature acquisition unit, used for performing image feature vectorization on all sub-graphs of the plurality of grid atlases to obtain a second feature vector set;

[0028] A grid mini-map matching unit, used for calculating the vector second norm of the second feature vector set and the first feature vector to obtain a grid mini-map in a plurality of grid atlases that minimizes the difference between the vector second norms;

[0029] The repositioning unit is used to load the center path trajectory point corresponding to the small grid map and use this point as the initial point of the robot.

[0030] Specifically, the laser point cloud frame acquisition unit includes: controlling the robot to rotate in situ to acquire a 360-degree laser point cloud frame.

[0031] Specifically, the laser point cloud frame acquisition unit specifically includes: using the IMU rotation angle yaw information to merge and splice the scanning frames scan of the laser radar during the rotation process into a laser point cloud frame S.

[0032] Specifically, the two-dimensional grid sub-image cur_map image feature vectorization performed in the two-dimensional grid sub-image feature acquisition unit and the two-dimensional grid sub-image cur_map image feature vectorization performed in the sub-grid map feature acquisition unit are both performed using DBOW3 for image feature vectorization.

[0033] In a third aspect, another embodiment of the present invention discloses a robot, comprising a central processing unit, a memory, and a laser radar, wherein instructions are stored in the memory, and the processor is used to implement the above method when executing the instructions.

[0034] In a fourth aspect, another embodiment of the present invention discloses a non-volatile memory, wherein instructions are stored in the memory, and the processor is used to implement the above method when executing the instructions.

[0035] The present invention divides the indoor large grid map into multiple small grid maps with different widths and heights, with the track point as the center, and relies on image information to find similar grids more easily, thereby achieving better and faster robot repositioning. Compared with the global traversal method, it is more robust and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 The embodiment of the present invention provides a two-dimensional grid schematic diagram;

[0038] Figure 2 is a schematic diagram of a robot relocation method based on a two-dimensional grid map provided by an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of a segmented small grid map provided by an embodiment of the present invention;

[0040] Figure 4 is a schematic diagram of a robot repositioning device based on a two-dimensional grid map provided by an embodiment of the present invention;

[0041] Figure 5 It is a schematic diagram of a robot repositioning device based on a two-dimensional grid map provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0043] Embodiment 1

[0044] refer to Figure 2 , this embodiment provides a robot relocalization method based on a two-dimensional grid map, which comprises the following steps:

[0045] S1, obtain the robot relocation instruction and load the robot path trajectory points saved during map building;

[0046] Specifically, when the position of the robot of this embodiment is lost, it is necessary to reposition it. Specifically, this implementation can automatically start the repositioning instruction when the position of the robot cannot be obtained.

[0047] S2, obtain the laser point cloud frame acquired by the robot;

[0048] The robot is equipped with a laser radar to obtain the surrounding environment, and each laser radar has a certain scanning angle.

[0049] The scanning range of the laser radar implemented in this embodiment is 270 degrees. However, in actual use, 360-degree environmental point cloud data needs to be obtained, and a certain angle needs to be rotated to obtain 360-degree environmental point cloud data.

[0050] In this embodiment, step S2 includes: controlling the robot to rotate in situ to obtain a 360-degree laser point cloud frame.

[0051] Specifically, the scanning range of the laser radar in this embodiment is 270 degrees. Therefore, by controlling the robot to rotate 90 degrees on the spot, 360-degree environmental point cloud data can be obtained.

[0052] If the laser radar carried by the robot has a scanning range of 180 degrees at a time, it needs to rotate 180 degrees to obtain the robot's 360-degree environmental point cloud data.

[0053] Specifically, this embodiment uses the IMU rotation angle yaw information to merge and splice the scanning frames scan of the laser radar during the rotation process into a laser point cloud frame S;

[0054]

[0055] in:

[0056] n-represents the number of laser scanning frames performed in step S2 to obtain 360-degree environmental data;

[0057] k- represents the kth laser scanning frame;

[0058] yaw k - represents the rotation angle of the robot during the k-th laser scanning frame, obtained by the IMU;

[0059] S3, converting the laser point cloud frame into a two-dimensional grid sub-map cur_map;

[0060] Specifically, the method of converting the laser point cloud frame into a two-dimensional grid sub-image in this embodiment belongs to the prior art and will not be described in detail in this embodiment.

[0061] S4, dividing the two-dimensional scene grid map into a plurality of grid atlases sub_maps with a width and height of M*M with each path trajectory point as the center;

[0062] refer to Figure 3 In this embodiment, the two-dimensional scene grid map is divided into multiple grid atlases sub_maps (1, 2, 3, ..., T) with a width and height of 200*200 with each path trajectory point as the center, where T represents the number of the multiple grid atlases divided.

[0063] S5, performing image feature vectorization on the two-dimensional grid sub-image cur_map to obtain a first feature vector cur_d;

[0064] This embodiment uses the DBOW3 library to vectorize the image features of cur_map to obtain a 64-dimensional feature vector cur_d;

[0065] S6, performing image feature vectorization on all sub-maps of the plurality of raster atlases sub_maps to obtain a second feature vector set d;

[0066] By calling the DBOW3 library, all sub-maps in sub_maps are vectorized to obtain a 64-dimensional feature vector set d;

[0067] S7, calculating the vector second norm of the second feature vector set and the first feature vector to obtain a grid mini-map in a plurality of grid atlases that minimizes the difference between the vector second norms;

[0068] Loop through and calculate the vector second norm ‖cur_d-di‖2, where i represents the i-th grid small map segmented from the navigation grid map, and find the i value that minimizes the difference between the two range numbers;

[0069] S8, loading the center path trajectory point corresponding to the small grid map, and using this point as the robot's initial point;

[0070] Load the center path trajectory point corresponding to the i-th small grid map, use this point as the robot's initial point, initialize it, and complete repositioning.

[0071] This embodiment divides the indoor large grid map into multiple small grid maps with different widths and heights, with the track point as the center, and makes it easier to find similar grids based on image information, thereby achieving better and faster robot relocation. Compared with the global traversal method, it is more robust and accurate.

[0072] Embodiment 2

[0073] refer to Figure 4 , this embodiment provides a robot repositioning device based on a two-dimensional grid map, which includes the following units:

[0074] The repositioning instruction acquisition unit is used to obtain the robot repositioning instruction and load the robot path trajectory points saved during map building;

[0075] Specifically, when the position of the robot of this embodiment is lost, it is necessary to reposition it. Specifically, this implementation can automatically start the repositioning instruction when the position of the robot cannot be obtained.

[0076] A laser point cloud frame acquisition unit, used to acquire the laser point cloud frame acquired by the robot;

[0077] The robot is equipped with a laser radar to obtain the surrounding environment, and each laser radar has a certain scanning angle.

[0078] The scanning range of the laser radar implemented in this embodiment is 270 degrees. However, in actual use, 360-degree environmental point cloud data needs to be obtained, and a certain angle needs to be rotated to obtain 360-degree environmental point cloud data.

[0079] The laser point cloud frame acquisition unit of this embodiment includes: controlling the robot to rotate in situ to acquire a 360-degree laser point cloud frame.

[0080] Specifically, the scanning range of the laser radar in this embodiment is 270 degrees. Therefore, by controlling the robot to rotate 90 degrees on the spot, 360-degree environmental point cloud data can be obtained.

[0081] If the laser radar carried by the robot has a scanning range of 180 degrees at a time, it needs to rotate 180 degrees to obtain the robot's 360-degree environmental point cloud data.

[0082] Specifically, this embodiment uses the IMU rotation angle yaw information to merge and splice the scanning frames scan of the laser radar during the rotation process into a laser point cloud frame S;

[0083]

[0084] in:

[0085] n-represents the number of laser scanning frames performed in step S2 to obtain 360-degree environmental data;

[0086] k- represents the kth laser scanning frame;

[0087] yaw k - represents the rotation angle of the robot during the k-th laser scanning frame, obtained by the IMU;

[0088] A two-dimensional grid sub-image acquisition unit, used for converting the laser point cloud frame into a two-dimensional grid sub-image cur_map;

[0089] Specifically, the method of converting the laser point cloud frame into a two-dimensional grid sub-image in this embodiment belongs to the prior art and will not be described in detail in this embodiment.

[0090] A sub-grid map acquisition unit is used to divide the two-dimensional scene grid map into a plurality of grid atlases sub_maps with a width and height of M*M with each path trajectory point as the center;

[0091] refer to Figure 3 In this embodiment, the two-dimensional scene grid map is divided into multiple grid atlases sub_maps (1, 2, 3, ..., T) with a width and height of 200*200 with each path trajectory point as the center, where T represents the number of the multiple grid atlases divided.

[0092] A two-dimensional grid sub-image feature acquisition unit, used for performing image feature vectorization on the two-dimensional grid sub-image cur_map to obtain a first feature vector cur_d;

[0093] This embodiment uses the DBOW3 library to vectorize the image features of cur_map to obtain a 64-dimensional feature vector cur_d;

[0094] A sub-grid map feature acquisition unit is used to perform image feature vectorization on all sub-maps of the plurality of grid atlases sub_maps to obtain a second feature vector set d;

[0095] By calling the DBOW3 library, all sub-images in sub_maps are vectorized to obtain a 64-dimensional feature vector set d;

[0096] A grid mini-map matching unit, used for calculating the vector second norm of the second feature vector set and the first feature vector to obtain a grid mini-map in a plurality of grid atlases that minimizes the difference between the vector second norms;

[0097] Loop through and calculate the vector second norm ‖cur_d-di‖2, where i represents the i-th grid small map segmented from the navigation grid map, and find the i value that minimizes the difference between the two range numbers;

[0098] A repositioning unit, used to load the center path trajectory point corresponding to the small grid map and use this point as the initial point of the robot;

[0099] Load the center path trajectory point corresponding to the i-th small grid map, use this point as the robot's initial point, initialize it, and complete repositioning.

[0100] This embodiment divides the indoor large grid map into multiple small grid maps with different widths and heights, with the track point as the center, and makes it easier to find similar grids based on image information, thereby achieving better and faster robot relocation. Compared with the global traversal method, it is more robust and accurate.

[0101] Embodiment 3

[0102] This embodiment discloses a robot, which includes a central processing unit, a memory, and a laser radar. The memory stores instructions, and the processor is used to implement a robot repositioning method based on a two-dimensional grid map when executing the instructions.

[0103] Embodiment 4

[0104] refer to Figure 5 , Figure 5 : is a schematic diagram of the structure of a robot repositioning device based on a two-dimensional grid map of this embodiment. The robot repositioning device based on a two-dimensional grid map of this embodiment 20 includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the above method embodiment are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module / unit in the above device embodiments are implemented.

[0105] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the robot repositioning device 20 based on the two-dimensional grid map. For example, the computer program may be divided into the modules in the second embodiment. For the specific functions of each module, please refer to the working process of the device described in the above embodiment, which will not be repeated here.

[0106] The robot repositioning device 20 based on the two-dimensional grid map may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of the robot repositioning device 20 based on the two-dimensional grid map, and does not constitute a limitation on the robot repositioning device 20 based on the two-dimensional grid map, and may include more or fewer components than shown in the figure, or combine certain components, or different components, for example, the robot repositioning device 20 based on the two-dimensional grid map may also include input and output devices, network access devices, buses, etc.

[0107] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the robot repositioning device 20 based on the two-dimensional grid map, and uses various interfaces and lines to connect various parts of the entire robot repositioning device 20 based on the two-dimensional grid map.

[0108] The memory 22 can be used to store the computer program and / or module. The processor 21 realizes various functions of the robot repositioning device 20 based on the two-dimensional grid map by running or executing the computer program and / or module stored in the memory 22 and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0109] Wherein, if the module / unit integrated in the robot repositioning device 20 based on the two-dimensional grid map is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, the steps of the above-mentioned various method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0110] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0111] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A robot relocation method based on a two-dimensional grid map, comprising the following steps: S1, obtain the robot relocation instruction and load the robot path trajectory points saved during map building; S2, obtain the robot's 360-degree laser point cloud frame; S3, converting the laser point cloud frame into a two-dimensional grid sub-map cur_map; S4, dividing the two-dimensional grid map into a plurality of grid atlases sub_maps with a width and height of M*M with each path trajectory point as the center; S5, performing image feature vectorization on the two-dimensional grid sub-image cur_map to obtain a first feature vector cur_d; S6, performing image feature vectorization on all sub-maps of the plurality of raster atlases sub_maps to obtain a second feature vector set d; S7, calculating the vector second norm of the second feature vector set and the first feature vector to obtain a grid mini-map in a plurality of grid atlases that minimizes the difference between the vector second norms; S8, loading the center path trajectory point corresponding to the grid map, and using this point as the robot's initial point; The step S5 performs image feature vectorization on the two-dimensional grid sub-image cur_map and the step S6 performs image feature vectorization on the two-dimensional grid sub-image cur_map, both of which use DBOW3 for image feature vectorization.

2. According to the method of claim 1, step S2 comprises: Control the robot to rotate in place and obtain a 360-degree laser point cloud frame.

3. According to the method of claim 2, step S2 specifically comprises: The IMU rotation angle yaw information is used to merge and splice the scanning frames scan of the laser radar during the rotation process into a laser point cloud frame S. 4 . The method according to claim 3 , wherein the feature vectors in the first feature vector and the second feature vector set are both 64-dimensional.

5. A robot repositioning device based on a two-dimensional grid map, comprising the following units: The repositioning instruction acquisition unit is used to obtain the robot repositioning instruction and load the robot path trajectory points saved during map building; A laser point cloud frame acquisition unit, used to acquire a 360-degree laser point cloud frame of the robot; A two-dimensional grid sub-image acquisition unit, used for converting the laser point cloud frame into a two-dimensional grid sub-image; A sub-grid map acquisition unit, used to divide the two-dimensional grid map into a plurality of grid atlases with a width and height of M*M with each path trajectory point as the center; A two-dimensional grid sub-image feature acquisition unit, used for performing image feature vectorization on the two-dimensional grid sub-image to obtain a first feature vector; A sub-grid map feature acquisition unit, used for performing image feature vectorization on all sub-graphs of the plurality of grid atlases to obtain a second feature vector set; A grid mini-map matching unit, used for calculating the vector second norm of the second feature vector set and the first feature vector to obtain a grid mini-map in a plurality of grid atlases that minimizes the difference between the vector second norms; A repositioning unit, used to load the center path trajectory point corresponding to the grid mini-map and use this point as the robot's initial point; The two-dimensional grid sub-image cur_map is vectorized in the two-dimensional grid sub-image feature acquisition unit, and the two-dimensional grid sub-image cur_map is vectorized in the sub-grid map feature acquisition unit, both of which use DBOW3 for image feature vectorization.

6. The device according to claim 5, wherein the laser point cloud frame acquisition unit comprises: Control the robot to rotate in place and obtain a 360-degree laser point cloud frame.

7. According to the device of claim 6, the laser point cloud frame acquisition unit specifically comprises: The IMU rotation angle yaw information is used to merge and splice the scanning frames scan of the laser radar during the rotation process into a laser point cloud frame S.

8. A robot comprising a central processing unit, a memory, and a laser radar, wherein the memory stores instructions, and the processor is used to implement the method described in any one of claims 1 to 4 when executing the instructions.

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