A mobile robot HDL global relocation method and system

Abstract

The application discloses a kind of mobile robot HDL global relocation method and system.The application utilizes UWB signal strength and time difference to determine the preliminary position of robot, and calculates the position probability distribution function.Combined with RANSAC algorithm, by multiplying the position probability distribution function with the initial uniform distribution of RANSAC, the fusion position distribution probability is obtained, which guides the RANSAC algorithm to focus on sampling near the UWB positioning result, and outputs the global relocation pose through multiple iterations and point cloud matching.The method reduces the positioning time of robot in the global relocation stage, improves the positioning accuracy, reduces the calculation amount and memory occupation.The application combines the advantages of UWB positioning and RANSAC point cloud matching, improves the global relocation accuracy and efficiency of mobile robot in complex environment.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of mobile robots, in particular to a mobile robot HDL global repositioning method and system. BACKGROUND

[0002] In the field of mobile robots, accurate global repositioning technology is the key to achieving efficient navigation and operation. Traditional global repositioning methods are mainly divided into direct global repositioning and global repositioning based on map matching.

[0003] Direct global repositioning methods rely on GNSS systems or UWB radio technology, but are limited by environmental factors and signal interference, making it difficult to achieve accurate positioning in indoor or complex environments. Global repositioning methods based on map matching, such as feature matching and RANSAC methods, can provide high positioning accuracy, but as the size of the map increases, the computational load and time consumption also increase, affecting the real-time performance and flexibility of the robot. SUMMARY

[0004] Based on this, the embodiments of the present application provide a mobile robot HDL global repositioning method and system. The present application realizes fast, accurate and efficient global repositioning by fusing UWB positioning and RANSAC point cloud matching technology.

[0005] In a first aspect, a mobile robot HDL global repositioning method is provided, which comprises:

[0006] Deploying UWB base stations in the working environment of the robot and ensuring that the robot can receive signals emitted by the base stations, determining the UWB positioning result of the robot based on the strength and time difference of the UWB signals;

[0007] Calculating the position probability distribution function according to the UWB positioning result;

[0008] Obtaining a fused position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm;

[0009] Using the fused position distribution probability to guide the RANSAC algorithm to perform key sampling near the UWB positioning result, and outputting the global repositioning pose through multiple iterations and point cloud matching.

[0010] Optionally, deploying UWB base stations in the working environment of the robot and ensuring that the robot can receive signals emitted by the base stations, determining the UWB positioning result of the robot based on the strength and time difference of the UWB signals, further comprises:

[0011] The robot's working environment is completely scanned using a LiDAR or RGBD camera, and a point cloud map of the environment is constructed using SLAM or 3D reconstruction methods. This point cloud map is then used as a global map for subsequent RANSAC matching.

[0012] Optionally, a location probability distribution function is calculated based on the UWB positioning results, specifically including:

[0013] Determine the distances between the robot and multiple base stations;

[0014] The objective function is optimized using the sum of squared errors of the distances between the robot and multiple base stations.

[0015] The location probability distribution function P is calculated by combining noise and error in the actual environment. UWB (x,y).

[0016] Optionally, the fused location distribution probability is obtained based on the location probability distribution function and the initial uniform distribution of the RANSAC algorithm, including:

[0017] The initial uniform distribution of the robot in the working environment is determined to be U(x,y);

[0018] The probability distribution of the fusion location is determined as P(x,y) = U(x,y)·P UWB (x,y); where the fused location distribution probability P(x,y) is used to characterize the new probability of the robot appearing at each location in the working environment under the influence of the UWB localization result.

[0019] Optionally, through multiple iterations and point cloud matching, the global relocalization pose is output, including:

[0020] After completing the iteration, the pose with the highest similarity to the current point cloud is found among the randomly sampled poses and output as the global relocalization result.

[0021] Optionally, the method further includes:

[0022] After global relocalization is completed, the obtained global pose is used as the accurate initial value for the real-time localization and navigation process, and the local precise localization algorithm based on NDT or ICP is executed cyclically. The cyclic execution of the local localization algorithm is interrupted only when the global localization is completely lost and the accurate current pose can no longer be obtained, and global relocalization is performed again.

[0023] Secondly, a mobile robot HDL global relocalization system is provided, the system comprising:

[0024] a UWB radio positioning module, configured to deploy UWB base stations in a working environment of the robot, and ensure that the robot can receive signals emitted by the base stations, and determine a UWB positioning result of the robot based on strengths and time differences of the UWB signals;

[0025] a RANSAC point cloud matching module, configured to calculate a position probability distribution function based on the UWB positioning result, obtain a fused position distribution probability based on the position probability distribution function and an initial uniform distribution of a RANSAC algorithm, and guide the RANSAC algorithm to perform intensive sampling near the UWB positioning result by using the fused position distribution probability, and output a global repositioning pose by multiple iterations and point cloud matching.

[0026] In a third aspect, an electronic device is provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the mobile robot HDL global repositioning method of any of the first aspect when executing the computer program.

[0027] In a fourth aspect, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the mobile robot HDL global repositioning method of any of the first aspect when executed by a processor.

[0028] In a fifth aspect, a computer program product is provided, including a computer program / instruction, and the computer program / instruction implementing the mobile robot HDL global repositioning method of any of the first aspect when executed by a processor.

[0029] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects: compared with positioning by only using a RANSAC point cloud matching algorithm, the global repositioning speed of the method is significantly improved, the occupied memory is significantly reduced, and the positioning accuracy is not reduced. Compared with radio positioning by only using UWB, the positioning accuracy of the final output of the method is significantly improved without increasing the deployment density of UWB hardware base stations. The present application combines the advantages of fast UWB positioning and high RANSAC positioning accuracy, while avoiding the disadvantages of both, and realizes fast and efficient global repositioning of the robot. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical schemes in the prior art, the drawings needed in the following embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only exemplary, and those skilled in the art can obtain other implementation drawings based on the provided drawings without creative labor.

[0031] Figure 1A step flow chart of a mobile robot HDL global repositioning method provided by an embodiment of the present application is shown in the figure;

[0032] Figure 2 An implementation schematic diagram of a mobile robot HDL global repositioning system provided by an optional embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0034] In the description of the present application, the terms "comprise", "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to only those steps or units explicitly listed, but can also include other steps or units inherent to the process, method, product or device, or steps or units added based on further optimization of the concept of the present application.

[0035] In the field of mobile robots, accurate global repositioning technology is the key to achieving efficient navigation and operation. Traditional global repositioning methods are mainly divided into two categories: direct global repositioning and global repositioning based on map matching.

[0036] Direct global repositioning method:

[0037] Directly obtaining the absolute position coordinates of the current robot.

[0038] Global repositioning based on GNSS system: based on Beidou or GPS GNSS system, through RTK differential antenna to receive GNSS global positioning system signal, which can directly obtain the absolute latitude and longitude coordinate system of the robot.

[0039] Global repositioning based on UWB radio technology: mainly using UWB and other positioning dedicated radio technology, combined with RFID, WiFi, BT or 4G / 5G general radio frequency communication technology for data transmission, and using radio base station as reference for direct radio positioning.

[0040] Global repositioning method based on map matching:

[0041] Firstly, SLAM technology is used to construct a 3D point cloud map of the target scene based on LiDAR, and the robot repositioning is performed by global matching with the constructed map to calculate the pose of the robot relative to the map coordinate system.

[0042] Characteristic matching method: taking a typical LIO-SAM-based global relocalization algorithm as an example, line and surface features of the current environment point cloud are extracted, features of all regions of the 3D point cloud map are traversed and matched with the current features, and the point in the map with the most similar features is taken as the pose of the current robot global positioning.

[0043] RANSAC method: taking the HDL-Localization positioning algorithm as an example, all poses of the 3D point cloud map are uniformly randomly sampled, the similarity of the current point cloud and the random sample is calculated, and the pose with the highest similarity is taken as the current robot pose.

[0044] The objective shortcomings of the direct global relocalization method include:

[0045] GNSS system-based global relocalization: GNSS system is limited by the satellite signals received by the antenna and cannot be used for indoor positioning. In environments such as forests, cities, and mountains, the positioning accuracy is limited due to satellite signal blockage, making it difficult to be used for precise navigation operations.

[0046] UWB radio technology-based global relocalization: The positioning accuracy of UWB radio positioning technology is related to the number of base stations and the deployment scenario. A large number of base stations will increase the cost, and objects such as rooms, walls, people, or vehicles in complex environments will also significantly reduce the positioning accuracy. Radio signal interference caused by complex electromagnetic environments in urban settings also affects the positioning accuracy.

[0047] The objective shortcomings of the map matching-based global relocalization method include:

[0048] Characteristic matching method: As the size of the map increases, the number of features will increase, resulting in a significant increase in the time spent on relocalization. Since the point cloud features contain less information, the likelihood of positioning errors is higher.

[0049] RANSAC method: The RANSAC method performs uniform random sampling on the global point cloud map, so as the size of the map increases, the number of sampling points also increases, resulting in a significant increase in the time spent on relocalization. The memory and computational load for relocalization also significantly increases, reducing the flexibility and real-time performance of navigation operations.

[0050] The present application is a mobile robot HDL global relocalization system and method that combines UWB radio positioning and RANSAC point cloud matching, specifically related to the HDL global relocalization technology, SLAM technology, and UWB radio positioning technology of mobile robots.

[0051] The purpose of the present application is to reduce the positioning time of the robot in the global relocation phase and improve the positioning accuracy, reduce the calculation amount and memory occupation of positioning, and solve the "robot kidnapping problem" in the engineering application of the robot. The robot positioning and navigation based on the method is essentially a global relocation method based on the positioning idea of three-layer HDL (Hierarchical Digital Localization). The method is a hierarchical method, which gradually narrows the positioning range by decomposing the environment into multiple levels of areas to improve accuracy and efficiency. The first layer is the preliminary positioning based on UWB radio positioning, with a positioning accuracy of about meters to tens of meters, and a positioning time of milliseconds. The second layer is the point cloud accurate positioning based on RANSAC, with a positioning accuracy of about meters to sub-meters, and a longer positioning time, which increases from about ten seconds to about one hundred seconds with the increase of the size of the environment point cloud map. The third layer is a point cloud-based accurate positioning algorithm with strong real-time performance, such as NDT or ICP, etc. The present patent only relates to the first layer and the second layer of the global relocation phase, and the robot generally only needs to perform global relocation for only once after starting or losing positioning, which takes a few seconds. The third layer is executed in a high frequency cycle after the completion of global relocation.

[0052] The system includes two parts of UWB radio positioning module and RANSAC point cloud matching module. The hardware part of the UWB radio positioning module includes UWB antenna and UWB radio receiver, and in addition, a proper amount of UWB base stations and their antennas are deployed in the working environment. The hardware of the RANSAC point cloud matching module is mainly the point cloud data acquisition LiDAR or RGBD camera, which can assist SLAM mapping and HDL positioning process through additional IMU. In addition, the system hardware also includes computer host and mobile robot chassis and other necessary components, and the UWB radio positioning algorithm and RANSAC point cloud matching algorithm run on the robot host.

[0053] Please refer to Figure 1 which shows a flowchart of a mobile robot HDL global relocation method provided by the embodiment of the present application, applied in the above-mentioned system, which can include the following steps:

[0054] S1, deploying UWB base stations in the working environment of the robot, and ensuring that the robot can receive the signals emitted by the base stations, and determining the UWB positioning result of the robot based on the strength and time difference of the UWB signals.

[0055] Among them, the working environment of the robot is completely scanned by using a laser radar or an RGBD camera, and a point cloud map of the environment is constructed by using SLAM or three-dimensional reconstruction method, and the point cloud map is taken as a global map for subsequent RANSAC matching.

[0056] S2, calculate the position probability distribution function according to the UWB positioning result.

[0057] wherein the distance between the robot and the plurality of base stations is determined;

[0058] based on the error sum of squares of the distance between the robot and the plurality of base stations as a target function, the target function is optimized using the LM algorithm;

[0059] and the position probability distribution function P(x,y) is calculated in combination with the noise and error in the actual environment UWB (x,y).

[0060] S3, obtain a fusion position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm.

[0061] wherein the initial uniform distribution of the robot in the working environment is determined as U(x,y);

[0062] the fusion position distribution probability P(x,y) is determined as P(x,y)=U(x,y)·P UWB (x,y); wherein the fusion position distribution probability P(x,y) is used to represent the new probability of the robot at each position in the working environment under the influence of the UWB positioning result.

[0063] In an optional embodiment of the present application, the distances of the robot relative to each UWB base station are measured and calculated by the UWB positioning system. By using these distance information and combining the coordinates of the base stations in the three-dimensional space, an equation set about the position of the robot can be established. By solving this equation set, the position estimation value of the robot can be obtained, and then the position probability distribution function is calculated according to the measurement error and environmental factors. This function describes the probability of the robot at each position in the space under the given UWB measurement result.

[0064] In the RANSAC algorithm, the initial pose sampling is based on the uniform distribution, which means that without prior knowledge, the algorithm assumes that the probability of the robot appearing at any position in the environment map is equal.

[0065] multiply the position probability distribution function P UWB (x,y) obtained by the UWB with the initial uniform distribution U(x,y) of the RANSAC algorithm to obtain a new fusion position distribution probability P(x,y)=U(x,y)·P UWB(x, y). This fused distribution probability reflects the new probability of the robot appearing at various locations in space under the influence of the UWB positioning results. In this way, the RANSAC algorithm is no longer completely random when sampling poses, but rather tends to sample in areas of high probability indicated by the UWB positioning results, thereby improving the efficiency and accuracy of the relocalization.

[0066] S4, using the fused position distribution probability to guide the RANSAC algorithm to focus on sampling near the UWB positioning results, and outputting the global relocalization pose through multiple iterations and point cloud matching.

[0067] Among the randomly sampled poses obtained after completing the iterations, the pose with the highest similarity to the current point cloud is found as the global relocalization result output.

[0068] By querying the nearest neighbor in the feature space, their corresponding points in the target point cloud are detected. This process involves feature matching, where only the matches that pass the pruning step are used to calculate the transformation and verified on the entire point cloud.

[0069] In addition, among the large number of randomly sampled poses obtained after completing the iterations, the pose with the highest similarity to the current point cloud is found as the global relocalization result output. This process involves accurate matching of point clouds and pose estimation, ultimately outputting an accurate global relocalization pose to provide an accurate initial position for the robot's navigation and operation.

[0070] The HDL global relocalization system and method described in this application fuses UWB radio positioning and RANSAC point cloud matching, as shown in the schematic diagram Figure 2 Before the robot performs the operation, preparation work is needed, including laying out an appropriate number of UWB base stations in the entire working environment of the robot and installing base station antennas, which ensures that the robot can receive a sufficient number of UWB base station signals at all possible poses and the signal strength can meet the basic measurement accuracy. In addition, a laser radar or RGBD camera is needed to completely scan the robot's working environment, and a SLAM or three-dimensional reconstruction method is used to construct the point cloud map of the environment, which serves as a global map for subsequent RANSAC matching.

[0071] Taking a two-dimensional plane (ground) constrained mobile robot as an example, the probability distribution function of the robot at a point on the two-dimensional plane is P(x, y). Without prior knowledge, the probability of the robot appearing at any coordinate point on the working plane after the robot system starts satisfies the two-dimensional uniform distribution U(x, y). First, UWB radio positioning is performed, and the robot's current UWB-based position probability distribution function P UWB(x, y), which indicates the probability of the robot appearing at a certain point in the map acquired by UWB, and is no longer uniform. Then the robot performs RANSAC point cloud matching according to the LiDAR point cloud data. For the traditional method, the pose random sampling probability of the RANSAC algorithm is a two-dimensional uniform distribution U(x, y) of the initial state. However, for this method, since the low-precision robot position probability distribution function has been acquired at this time, the pose random sampling probability P(x, y) of the RANSAC algorithm = U(x, y) P UWB (x, y), that is, there is a greater probability of sampling the area around the position acquired by UWB. That is, under the same positioning accuracy, the number of iterations required by RANSAC after fusing UWB is significantly reduced. After completing the iteration, the pose with the highest similarity to the current point cloud can be found among the large number of randomly sampled poses as the global repositioning result output.

[0072] In an optional embodiment, in a three-dimensional environment containing a direction angle, the probability of the pose is P(x, y, z, roll, pitch, yaw). The RANSAC algorithm will consider a six-dimensional space of position and direction, and through an iterative process, the pose with the highest similarity to the current point cloud is found as the global repositioning result output. After completing the iteration, the pose with the highest similarity to the current point cloud, including the position (x, y, z) and the direction (roll, pitch, yaw), can be found among the large number of randomly sampled poses as the global repositioning result output.

[0073] After completing the global repositioning, the acquired global pose is taken as the accurate initial value of the real-time positioning and navigation process. The system cyclically executes the local precise positioning algorithm based on NDT or ICP at a high frequency, and no longer performs global repositioning. Only when the global positioning is completely lost and an accurate current pose can no longer be acquired, the system will interrupt the cyclic execution of the local positioning algorithm and re-perform global repositioning.

[0074] As can be seen from the above, the innovation of the present application lies in:

[0075] The global repositioning method based on HDL (hierarchical digital localization): based on a hierarchical method, the environment is decomposed into multiple hierarchical regions, and the positioning range is gradually narrowed to improve the accuracy and efficiency.

[0076] The preliminary positioning based on UWB: the UWB radio positioning technology takes the radio base station as a reference to perform radio positioning based on geometric relationship, signal strength and ToF, and provides an initial pose probability distribution of the robot with a meter-level error.

[0077] Fine positioning based on RANSAC point cloud matching: the initialization random sampling probability of the RANSAC algorithm is no longer a uniform probability density distribution, but a probability density distribution containing positioning information generated by UWB for random pose sampling, followed by multiple iterations and point cloud matching, and finally outputting an accurate global repositioning pose.

[0078] The embodiment of the application also provides a mobile robot HDL global repositioning system, which can comprise:

[0079] A UWB radio positioning module is configured to deploy UWB base stations in the working environment of the robot and ensure that the robot can receive signals emitted by the base stations, and determine the UWB positioning result of the robot based on the strength and time difference of the UWB signals;

[0080] A RANSAC point cloud matching module is configured to calculate a position probability distribution function according to the UWB positioning result, obtain a fused position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm, guide the RANSAC algorithm to perform key sampling near the UWB positioning result by using the fused position distribution probability, and output a global repositioning pose through multiple iterations and point cloud matching.

[0081] The specific definition of the mobile robot HDL global repositioning system can be referred to the definition of the mobile robot HDL global repositioning method in the foregoing, which will not be described here. Each module in the mobile robot HDL global repositioning system described above can be realized by software, hardware and a combination thereof in whole or in part. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0082] In one embodiment, an electronic device, which can be a computer, is provided. The electronic device comprises a processor, a memory and a network interface connected by a system bus. The processor of the device is configured to provide computing and control capabilities. The memory of the device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store mobile robot HDL global repositioning data. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a mobile robot HDL global repositioning method.

[0083] In one embodiment, a computer readable storage medium having a computer program stored thereon is also provided, which involves all or part of the processes in the above-mentioned embodiment method.

[0084] In one embodiment, a computer program product is also provided, including computer programs / instructions, relating to all or part of the processes of the above-mentioned embodiments.

[0085] A person of ordinary skill in the art can understand that implementing all or part of the processes of the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in several forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (SyMchliMk) DRAM (SLDRAM), memory bus (RaMbus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0086] Any combination of the technical features of the above-mentioned embodiments can be combined. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0087] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent application. It should be noted that for a person of ordinary skill in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the scope of the patent protection of the present application should be subject to the appended claims.

Claims

1. A mobile robot HDL global relocalization method, characterized by, The method comprises: deploying UWB base stations in the working environment of the robot and ensuring that the robot can receive signals emitted by the base stations, and determining the UWB positioning result of the robot based on the strength and time difference of the UWB signals; calculating a position probability distribution function according to the UWB positioning result; obtaining a fused position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm; using the fused position distribution probability to guide the RANSAC algorithm to perform key sampling near the UWB positioning result, and outputting a global repositioning pose through multiple iterations and point cloud matching; deploying UWB base stations in the working environment of the robot and ensuring that the robot can receive signals emitted by the base stations, and determining the UWB positioning result of the robot based on the strength and time difference of the UWB signals, further comprising: using a laser radar or an RGBD camera to completely scan the working environment of the robot, and using a SLAM or three-dimensional reconstruction method to construct a point cloud map of the environment, and using the point cloud map as a global map for subsequent RANSAC matching; According to the UWB positioning result, a position probability distribution function is calculated, specifically including: determining distances between the robot and multiple base stations; using the LM algorithm to optimize a target function based on error squares of the distances between the robot and the multiple base stations; and combining noise and errors in an actual environment to calculate the position probability distribution function P UWB (x,y); obtaining a fused position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm, comprising: determining the initial uniform distribution of the robot in the working environment as U(x, y); The fusion position distribution probability P(x, y) is determined as P(x, y) = U(x, y) P UWB (x, y); wherein the fusion position distribution probability P(x, y) is used to represent the new probability of the robot appearing at each position in the working environment under the influence of the UWB positioning result.

2. The mobile robot HDL global relocalization method of claim 1, wherein, outputting a global repositioning pose through multiple iterations and point cloud matching, comprising: finding the pose with the highest similarity to the current point cloud from the randomly sampled poses after completing the iterations as the global repositioning result output.

3. The mobile robot HDL global relocalization method of claim 1, wherein, The method further comprises: after completing the global repositioning, the global pose obtained is used as an accurate initial value of a real-time positioning and navigation process, and a local precise positioning algorithm based on NDT or ICP is executed in a loop; wherein, only when the global positioning is completely lost and an accurate current pose can no longer be obtained, the loop execution of the local positioning algorithm is interrupted, and global repositioning is performed again.

4. A mobile robot HDL global repositioning system, characterized by, The system comprises: a UWB radio positioning module for deploying UWB base stations in the working environment of the robot and ensuring that the robot can receive signals emitted by the base stations, and determining the UWB positioning result of the robot based on the strength and time difference of the UWB signals; a RANSAC point cloud matching module for calculating a position probability distribution function according to the UWB positioning result; obtaining a fused position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm; using the fused position distribution probability to guide the RANSAC algorithm to perform key sampling near the UWB positioning result, and outputting a global repositioning pose through multiple iterations and point cloud matching; deploying UWB base stations in the working environment of the robot and ensuring that the robot can receive signals emitted by the base stations, and determining the UWB positioning result of the robot based on the strength and time difference of the UWB signals, further comprising: using a laser radar or an RGBD camera to completely scan the working environment of the robot, and using a SLAM or three-dimensional reconstruction method to construct a point cloud map of the environment, and using the point cloud map as a global map for subsequent RANSAC matching; According to the UWB positioning result, a position probability distribution function is calculated, specifically including: determining distances between the robot and multiple base stations; using the LM algorithm to optimize a target function based on error squares of the distances between the robot and the multiple base stations; and combining noise and errors in an actual environment to calculate the position probability distribution function P UWB (x,y); Fusing the position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm, comprising: determining an initial uniform distribution U(x, y) of the robot in the working environment; The fusion position distribution probability P(x, y) is determined as P(x, y) = U(x, y) P UWB (x, y); wherein the fusion position distribution probability P(x, y) is used to represent the new probability of the robot appearing at each position in the working environment under the influence of the UWB positioning result.

5. An electronic device, comprising: A computer program product, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, A computer program product, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 3.

7. A computer program product comprising computer programs / instructions, characterized in that, A computer program product, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 3.

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