Mobile robot HDL global repositioning method and system

Abstract

The invention discloses a mobile robot HDL global relocation method and system. According to the invention, the initial position of the robot is determined by using the UWB signal intensity and the time difference, and the position probability distribution function is calculated. In combination with an RANSAC algorithm, a position probability distribution function is multiplied by initial uniform distribution of the RANSAC to obtain a fusion position distribution probability, the RANSAC algorithm is guided to carry out key sampling near a UWB positioning result, and a global repositioning pose is output through multiple iterations and point cloud matching. According to the method, the positioning time of the robot in the global repositioning stage is shortened, the positioning precision is improved, and the calculated amount and memory occupation are reduced. According to the invention, the advantages of UWB positioning and RANSAC point cloud matching are combined, and the global repositioning precision and efficiency of the mobile robot in a complex environment are improved.

Description

Technical Field

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

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

[0003] Direct global relocalization 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 relocalization methods based on map matching, such as feature matching and RANSAC methods, can provide higher positioning accuracy, but as the map size increases, the amount of calculation and time consumption also increase, affecting the real-time performance and flexibility of the robot. Summary of the invention

[0004] Based on this, an embodiment of the present application provides a mobile robot HDL global repositioning method and system. The present application achieves fast, accurate and efficient global repositioning by integrating UWB positioning and RANSAC point cloud matching technology.

[0005] In a first aspect, a mobile robot HDL global relocalization method is provided, the method comprising:

[0006] Deploy a UWB base station in the robot's working environment and ensure that the robot can receive the signal sent by the base station. Determine the robot's UWB positioning result based on the strength and time difference of the UWB signal.

[0007] Calculate the position probability distribution function based on the UWB positioning results;

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

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

[0010] Optionally, a UWB base station is deployed in the working environment of the robot, and the robot is ensured to receive a signal sent by the base station, and the UWB positioning result of the robot is determined based on the strength and time difference of the UWB signal, further comprising:

[0011] Use LiDAR or RGBD camera to completely scan the robot working environment, and use SLAM or 3D reconstruction method to build a point cloud map of the environment, and use the point cloud map as the global map for subsequent RANSAC matching.

[0012] Optionally, calculating a position probability distribution function according to the UWB positioning result specifically includes:

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

[0014] The objective function is optimized using the LM algorithm based on the sum of square errors of the distances between the robot and multiple base stations.

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

[0016] Optionally, obtaining a fusion position distribution probability according to the position probability distribution function and an initial uniform distribution of a RANSAC algorithm includes:

[0017] Determine the initial uniform distribution of the robot in the working environment as U(x,y);

[0018] Determine the fusion position distribution probability P(x,y) as P(x,y)=U(x,y)·P UWB (x, y); where the fused position distribution probability P(x, y) is used to characterize the new probability of the robot appearing at various positions in the working environment under the influence of the UWB positioning results.

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

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

[0021] Optionally, the method further comprises:

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

[0023] In a second aspect, a mobile robot HDL global repositioning system is provided, the system comprising:

[0024] The UWB radio positioning module is used to deploy a UWB base station in the robot's working environment and ensure that the robot can receive the signal sent by the base station, and determine the robot's UWB positioning result based on the strength and time difference of the UWB signal;

[0025] The RANSAC point cloud matching module is used to calculate the position probability distribution function according to the UWB positioning result; obtain the fused position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm; use the fused position distribution probability to guide the RANSAC algorithm to perform key sampling near the UWB positioning result, and output the global repositioning posture through multiple iterations and point cloud matching.

[0026] In a third aspect, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the mobile robot HDL global repositioning method described in any one of the first aspects is implemented.

[0027] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the mobile robot HDL global repositioning method described in any one of the first aspects is implemented.

[0028] In a fifth aspect, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the mobile robot HDL global repositioning method described in any one of the first aspects.

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

[0030] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0031] Figure 1A flowchart of the steps of a mobile robot HDL global relocation method provided in an embodiment of the present application;

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

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

[0034] In the description of the present invention, the terms "comprises", "has" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may also include other steps or units that are not explicitly listed but are inherent to these processes, methods, products or apparatuses, or steps or units added based on further optimization schemes conceived by the present invention.

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

[0036] Direct global relocation method:

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

[0038] Global repositioning based on GNSS system: Based on GNSS systems such as Beidou or GPS, the GNSS global positioning system signals are received through the RTK differential antenna to directly obtain the absolute latitude and longitude coordinate system of the robot.

[0039] Global repositioning based on UWB radio technology: Based on UWB and other positioning-specific radio technologies, combined with general RF communication technologies for data transmission such as RFID, WiFi, BT or 4G / 5G, direct radio positioning is performed with RF base stations as reference.

[0040] Global relocalization method based on map matching:

[0041] First, SLAM technology is used to build a 3D point cloud map of the target scene based on LiDAR. When the robot is relocated, the constructed map is used for global matching to calculate the robot's position relative to the map coordinate system.

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

[0043] RANSAC method: Taking the HDL-Localization positioning algorithm as an example, all postures of the 3D point cloud map are uniformly randomly sampled, and the similarity between the current point cloud and the random samples is calculated. The posture with the highest similarity is taken as the current robot posture.

[0044] Among them, the objective disadvantages of the direct global relocation method include:

[0045] Global repositioning based on the GNSS system: The GNSS system is limited by the satellite signals received by the antenna and cannot be positioned indoors. In environments such as forests, cities, and mountains, the satellite signals are blocked, and the positioning accuracy is limited, making it difficult to use for precise navigation operations.

[0046] Global repositioning based on UWB radio technology: The positioning accuracy of UWB radio positioning technology is related to the number of base stations and deployment scenarios. The deployment of a large number of base stations will increase costs, and objects such as rooms, walls, people or vehicles in complex environments will also significantly reduce positioning accuracy. The radio signal interference caused by the complex electromagnetic environment in the urban background also affects the positioning accuracy.

[0047] The objective disadvantages of global relocalization methods based on map matching include:

[0048] Feature matching method: As the map size increases, the number of features will increase, resulting in a significant increase in the time spent on relocalization. In addition, since the point cloud features contain less information, there is a greater possibility of positioning errors.

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

[0050] The present application is a mobile robot HDL global repositioning system and method that integrates UWB radio positioning and RANSAC point cloud matching, and specifically relates to the mobile robot's HDL global repositioning technology, SLAM technology and UWB radio positioning technology.

[0051] The purpose of the present invention is to reduce the positioning time of the robot in the global relocalization stage and improve the positioning accuracy, reduce the calculation amount and memory usage of positioning, and solve the "robot kidnapping problem" in robot engineering applications. The robot positioning navigation based on this method is essentially a global relocalization method based on the three-layer HDL (Hierarchical Digital Localization) positioning idea. This method is a layered method that gradually narrows the scope of positioning by decomposing the environment into multiple levels of areas to improve accuracy and efficiency. The first layer is a preliminary positioning based on UWB radio positioning, with a positioning accuracy of about meters to ten meters and a positioning time of milliseconds. The second layer is a point cloud precise positioning based on RANSAC, with a positioning accuracy of about meters to sub-meters, and a long positioning time. As the size of the environmental point cloud map increases, the positioning time increases from about ten seconds to hundreds of seconds. The third layer is a point cloud-based precise positioning algorithm with strong real-time performance, such as NDT or ICP. This patent only involves the first and second layers of the global relocalization stage. The robot generally only needs to spend a few seconds to perform global relocalization only once after startup or after positioning is lost. The third layer is executed cyclically at a high frequency after the global relocation is completed.

[0052] The system consists of two parts: the UWB radio positioning module and the RANSAC point cloud matching module. The hardware part of the UWB radio positioning module includes the UWB antenna and the UWB radio receiver. In addition, an appropriate number of UWB base stations and their antennas need to be deployed in the working environment. The hardware of the RANSAC point cloud matching module is mainly for obtaining point cloud data LiDAR or RGBD camera, and an additional IMU can be used to assist the SLAM mapping and HDL positioning process. In addition, the system hardware also includes necessary components such as the computer host and the mobile robot chassis. The UWB radio positioning algorithm and the RANSAC point cloud matching algorithm run on the robot host.

[0053] Please refer to Figure 1 , which shows a flow chart of a mobile robot HDL global relocation method provided by an embodiment of the present application, which is applied to the above system and may include the following steps:

[0054] S1, deploy a UWB base station in the robot's working environment and ensure that the robot can receive the signal sent by the base station, and determine the robot's UWB positioning result based on the strength and time difference of the UWB signal.

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

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

[0057] Among them, determining the distance between the robot and multiple base stations;

[0058] The objective function is optimized using the LM algorithm based on the sum of square errors of the distances between the robot and multiple base stations.

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

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

[0061] Among them, the initial uniform distribution of the robot in the working environment is determined to be U(x,y);

[0062] Determine the fusion position distribution probability P(x,y) as P(x,y)=U(x,y)·P UWB (x, y); where the fused position distribution probability P(x, y) is used to characterize the new probability of the robot appearing at various positions in the working environment under the influence of the UWB positioning results.

[0063] In an optional embodiment of the present application, the distance of the robot relative to each UWB base station is measured and calculated by a UWB positioning system. Using this distance information, combined with the coordinates of the base station in three-dimensional space, a set of equations about the robot's position can be established. By solving this set of equations, the robot's position estimate can be obtained, and then the position probability distribution function can be calculated based on the measurement error and environmental factors. This function describes the probability of the robot appearing at various positions in space given the UWB measurement results.

[0064] In the RANSAC algorithm, the initial pose sampling is based on a 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] The position probability distribution function P obtained by UWB UWB (x, y) is multiplied by 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 positions in space under the influence of the UWB positioning results. In this way, when the RANSAC algorithm performs pose sampling, it is no longer completely random, but tends to sample in the high-probability area indicated by the UWB positioning results, thereby improving the efficiency and accuracy of relocalization.

[0066] S4 uses the fused position distribution probability to guide the RANSAC algorithm to perform key sampling near the UWB positioning result, and outputs the global repositioning pose through multiple iterations and point cloud matching.

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

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

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

[0070] The schematic diagram of the HDL global relocalization system and method for integrating UWB radio positioning and RANSAC point cloud matching described in this application is as follows Figure 2 As shown. Before the robot performs the operation, preparation work needs to be done, including deploying an appropriate number of UWB base stations and installing base station antennas in the entire working environment of the robot. This ensures that the robot can receive a sufficient number of UWB base station signals in all possible positions, and the signal strength can meet the basic measurement accuracy. In addition, it is necessary to use a lidar or RGBD camera to completely scan the robot's working environment, and use methods such as SLAM or 3D reconstruction to build a point cloud map of the environment, with the purpose of serving as a global map for subsequent RANSAC matching.

[0071] Taking a mobile machine constrained by a two-dimensional plane (ground) 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 system appearing at any coordinate point on the working plane after startup satisfies the two-dimensional uniform distribution U(x, y). First, perform UWB radio positioning to obtain the robot's current UWB-based position probability distribution function P UWB(x, y), this probability distribution indicates the probability of the robot obtained by UWB appearing at a certain point in the map, and it is no longer uniform. The robot then performs RANSAC point cloud matching based on the LiDAR point cloud data. For traditional methods, the random sampling probability of the RANSAC algorithm is the two-dimensional uniform distribution U(x, y) of the initial state. However, for this method, since a lower-precision robot position probability distribution function has been obtained at this time, the random sampling probability of the RANSAC algorithm is P(x, y) = U(x, y)·P UWB (x, y), that is, there is a greater probability to sample the area near the position obtained by UWB. That is, under the same positioning accuracy, the number of iterations required by RANSAC after integrating UWB is significantly reduced. After the iteration is completed, the pose with the highest similarity to the current point cloud can be found among a large number of randomly sampled poses as the global relocalization result output.

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

[0073] After completing global relocalization, the global pose obtained is used 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 higher frequency, and no longer performs global relocalization. Only when the global positioning is completely lost and the accurate current pose can no longer be obtained, the system will interrupt the cyclic execution of the local positioning algorithm and re-perform global relocalization.

[0074] From the above, it can be seen that the innovation of the present invention is:

[0075] Global relocalization method based on HDL (hierarchical digital localization): Based on the hierarchical method, the scope of positioning is gradually narrowed by decomposing the environment into multiple levels of areas to improve accuracy and efficiency.

[0076] UWB-based preliminary positioning: UWB radio positioning technology uses the RF base station as a reference to perform radio positioning based on geometric relationships, signal strength and ToF, providing the robot's initial posture probability distribution with a meter-level error.

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

[0078] The present application also provides a mobile robot HDL global repositioning system, which may include:

[0079] The UWB radio positioning module is used to deploy a UWB base station in the robot's working environment and ensure that the robot can receive the signal sent by the base station, and determine the robot's UWB positioning result based on the strength and time difference of the UWB signal;

[0080] The RANSAC point cloud matching module is used to calculate the position probability distribution function according to the UWB positioning result; obtain the fused position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm; use the fused position distribution probability to guide the RANSAC algorithm to perform key sampling near the UWB positioning result, and output the global repositioning posture through multiple iterations and point cloud matching.

[0081] For the specific definition of the mobile robot HDL global repositioning system, please refer to the definition of the mobile robot HDL global repositioning method in the above text, which will not be repeated here. Each module in the above mobile robot HDL global repositioning system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0082] In one embodiment, an electronic device is provided, which may be a computer. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the device is used to provide computing and control capabilities. The memory of the device includes 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 operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for mobile robot HDL global relocation data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a mobile robot HDL global relocation method is implemented.

[0083] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, 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 a computer program / instruction, which involves all or part of the process in the above embodiment method.

[0085] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, 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 embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in M ​​forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), 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), etc.

[0086] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A mobile robot HDL global relocation method, characterized in that: The method comprises: Deploy a UWB base station in the robot's working environment and ensure that the robot can receive the signal sent by the base station. Determine the robot's UWB positioning result based on the strength and time difference of the UWB signal. Calculate the position probability distribution function based on the UWB positioning results; Obtaining a fusion position distribution probability according to the position probability distribution function and an initial uniform distribution of the RANSAC algorithm; The fused position distribution probability is used to guide the RANSAC algorithm to perform key sampling near the UWB positioning result, and the global repositioning pose is output through multiple iterations and point cloud matching.

2. The mobile robot HDL global relocation method according to claim 1, characterized in that: Deploy a UWB base station in the robot's working environment and ensure that the robot can receive the signal from the base station. Determine the robot's UWB positioning result based on the strength and time difference of the UWB signal, and also include: Use LiDAR or RGBD camera to completely scan the robot working environment, and use SLAM or 3D reconstruction method to build a point cloud map of the environment, and use the point cloud map as the global map for subsequent RANSAC matching.

3. The mobile robot HDL global relocation method according to claim 1, characterized in that: The position probability distribution function is calculated based on the UWB positioning results, including: Determine the distance between the robot and multiple base stations; The objective function is optimized using the LM algorithm based on the sum of square errors of the distances between the robot and multiple base stations. The position probability distribution function P is calculated by combining the noise and error in the actual environment. UWB (x,y).

4. The mobile robot HDL global relocation method according to claim 3, characterized in that: The fusion position distribution probability is obtained according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm, including: Determine the initial uniform distribution of the robot in the working environment as U(x,y); Determine the fusion position distribution probability P(x,y) as P(x,y)=U(x,y)·P UWB (x, y); where the fused position distribution probability P(x, y) is used to characterize the new probability of the robot appearing at various positions in the working environment under the influence of the UWB positioning results.

5. The mobile robot HDL global relocation method according to claim 1, characterized in that: After multiple iterations and point cloud matching, the global relocation pose is output, including: After completing the iteration, the randomly sampled poses are found and the pose with the highest similarity to the current point cloud is output as the global relocalization result.

6. The mobile robot HDL global relocation method according to claim 1, characterized in that: The method further comprises: After completing the global relocalization, the obtained global pose is used as the accurate initial value of the real-time positioning and navigation process, and the local precise positioning algorithm based on NDT or ICP is executed cyclically. Only when the global positioning is completely lost and the accurate current pose can no longer be obtained, the cyclic execution of the local positioning algorithm is interrupted and the global relocalization is performed again.

7. A mobile robot HDL global repositioning system, characterized in that: The system comprises: The UWB radio positioning module is used to deploy a UWB base station in the robot's working environment and ensure that the robot can receive the signal sent by the base station, and determine the robot's UWB positioning result based on the strength and time difference of the UWB signal; The RANSAC point cloud matching module is used to calculate the position probability distribution function according to the UWB positioning result; obtain the fused position distribution probability according to the position probability distribution function and the initial uniform distribution of the RANSAC algorithm; use the fused position distribution probability to guide the RANSAC algorithm to perform key sampling near the UWB positioning result, and output the global repositioning posture through multiple iterations and point cloud matching.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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