Autonomous positioning method and system for ground platform guided by prior information of unmanned aerial vehicle

Abstract

The present application relates to a method and system for autonomous positioning of a ground platform under the guidance of prior information of an unmanned aerial vehicle. The method comprises: transmitting the prior point cloud map constructed by the unmanned aerial vehicle and its corresponding reference base point coordinates back to the ground platform, and performing map area block and encoding storage; initializing the positioning of the ground platform based on the GNSS data acquired by the ground platform; after completing the positioning initialization, using an improved iterative error Kalman filter algorithm, combined with the laser radar point cloud data and IMU inertial data acquired by the ground platform, iteratively estimating the real-time posture of the ground platform; calculating the distance between the real-time position of the ground platform and the boundary of the local map based on the real-time posture of the ground platform, if the distance is less than the preset distance threshold, then updating the local map corresponding to the area where the ground platform is located, and realizing autonomous positioning of the ground platform. This method can realize high-precision autonomous positioning of the ground platform in complex and dynamic environments.

Description

Technical Field

[0001] The present application relates to the technical field of autonomous navigation and positioning of ground unmanned platforms, and in particular to a method and system for autonomous positioning of ground platforms under the guidance of prior information of unmanned aerial vehicles. Background Art

[0002] With the rapid development of unmanned platform technology, the application of autonomous platforms such as unmanned vehicles and drones in field operations has become more and more extensive. In complex field environments, accurate and robust positioning technology is the core to ensure that ground unmanned platforms can complete autonomous tasks. However, although the current positioning methods based on point cloud maps or visual maps can achieve a certain degree of environmental perception, most methods rely on ground platforms to generate maps by themselves. This process is not only computationally intensive, but also in dynamic environments or areas with complex terrain, the accuracy and integrity of real-time mapping cannot be guaranteed. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for autonomous positioning of a ground platform under the guidance of UAV prior information to address the above technical problems. By combining the prior map information generated by the UAV with the real-time sensor data of the ground platform, and using positioning initialization and efficient point cloud registration algorithms, high-precision autonomous positioning of the ground platform in complex and dynamic environments can be achieved.

[0004] A method for autonomous positioning of a ground platform under the guidance of prior information of an unmanned aerial vehicle, the method comprising:

[0005] Use drones to build a priori point cloud map, and transmit the priori point cloud map and its corresponding reference point coordinates back to the ground platform for map area segmentation and encoding storage;

[0006] Based on the sensors of the ground platform itself, the laser radar point cloud data, IMU data and GNSS data are obtained. The initial position of the ground platform and the local map corresponding to the initial location of the ground platform are obtained according to the GNSS data. The positioning initialization of the ground platform is completed by aligning the laser radar point cloud data with the local map.

[0007] After the LiDAR point cloud data and the IMU data are time-aligned, the predicted position and posture of the ground platform are obtained by recursively extrapolating the time-aligned IMU data, and the LiDAR point cloud data is dedistorted using the predicted position and posture.

[0008] The dedistorted LiDAR point cloud data is converted to the global coordinate system according to the predicted pose, and the converted LiDAR point cloud data is iteratively registered with the local map until the iteration termination condition is met. During the iterative registration process, the state space of the ground platform is updated, and the final estimated pose is output as the real-time pose of the ground platform.

[0009] The distance between the real-time position of the ground platform and the boundary of the local map is calculated based on the real-time position of the ground platform. If the distance is less than the preset distance threshold, the local map corresponding to the area where the ground platform is located is updated to achieve autonomous positioning of the ground platform.

[0010] In one embodiment, a priori point cloud map is constructed by using a drone, and the priori point cloud map and its corresponding reference point coordinates are transmitted back to a ground platform for map area segmentation and encoding storage, including:

[0011] The multi-dimensional environmental data in the mission area is collected by the perception sensors carried by the drone. The collected data is fused and processed to generate a priori point cloud map. The point cloud pose of the prior point cloud map is calibrated using the inertial navigation system carried by the drone to ensure that the prior point cloud map is aligned to the global coordinate system.

[0012] According to the wireless communication module carried by the UAV, the a priori point cloud map and its corresponding reference benchmark point coordinates are transmitted back to the ground platform in real time. On the ground platform side, the a priori point cloud map is divided into regions using a grid division algorithm, and a unique coding identifier is generated for the local map corresponding to each region based on the reference benchmark point coordinates, thereby realizing the coded storage of each local map.

[0013] In one embodiment, the initial position and posture of the ground platform and the local map corresponding to the initial area of ​​the ground platform are obtained according to the GNSS data, and the positioning initialization of the ground platform is completed by aligning the laser radar point cloud data with the local map, including:

[0014] The initial posture of the ground platform is obtained according to the GNSS data. The reference coordinates of the ground platform in the prior point cloud map are calculated based on the initial posture. The local map corresponding to the initial area of ​​the ground platform is extracted according to the reference coordinates. The lidar point cloud data is aligned with the local map using the point cloud matching algorithm to complete the initialization of the ground platform positioning.

[0015] In one embodiment, after the LiDAR point cloud data and the IMU data are time-aligned, the predicted position and posture of the ground platform are obtained by recursively performing the time-aligned IMU data, including:

[0016] Time alignment is performed based on the timestamps contained in both the LiDAR point cloud data and the IMU data, and the time-aligned IMU data is recursively extrapolated to obtain the predicted position and posture of the ground platform, and the state prior of the ground platform is updated; the state space of the ground platform is Expressed as

[0017] ;

[0018] in, is the location of the ground platform, is the speed of the ground platform, is the posture of the ground platform, and are the IMU acceleration and gyroscope bias respectively, is the gravity vector.

[0019] In one embodiment, the dedistorted LiDAR point cloud data is converted to a global coordinate system according to the predicted pose, and the converted LiDAR point cloud data is iteratively registered with the local map until an iteration termination condition is met, and during the iterative registration process, the state space of the ground platform is updated, and the final estimated pose is output as the real-time pose of the ground platform, including:

[0020] According to the predicted pose, the dedistorted lidar point cloud data is converted to the global coordinate system. The improved iterative error Kalman filter algorithm is used to iteratively align the converted lidar point cloud data with the local map until the alignment error is less than the preset threshold or the number of iterations exceeds the preset maximum value. During the iterative alignment process, the state space of the ground platform is updated in real time, and the final estimated pose is output as the real-time pose of the ground platform.

[0021] In one of the embodiments, the local map is dynamically updated using a sliding window mechanism, and each local map is stored in a block format, and each map sub-block obtained by blocking a single local map is numbered according to its global Gaussian coordinates.

[0022] In one embodiment, the local map is dynamically updated using a sliding window mechanism, including:

[0023] In the ground platform positioning initialization stage, 9 map sub-blocks in the local map corresponding to the initial location of the ground platform are loaded, and the global Gaussian coordinates of the central map sub-block are calculated;

[0024] During the autonomous positioning process of the ground platform, determine the global positioning result of the ground platform at the current moment With the current center map subblock The position relationship, when exist If it is outside the range, according to and The relative position relationship between the ground platform and the center is determined, and the corresponding direction is selected for map update. The three map sub-blocks in the direction opposite to the ground platform are released, and three new map sub-blocks are loaded along the forward direction of the ground platform. At the next moment, the position relationship between the global positioning result of the ground platform and the center map sub-block is continued to be determined. exist When within the bounds, keep the map unchanged.

[0025] A ground platform autonomous positioning system guided by prior information of unmanned aerial vehicles, the system comprising:

[0026] The prior information acquisition module is used to construct a priori point cloud map using the UAV, and transmit the priori point cloud map and its corresponding reference point coordinates back to the ground platform for map area segmentation and encoding storage;

[0027] The positioning initialization module is used to obtain the lidar point cloud data, IMU data and GNSS data based on the ground platform's own sensors, obtain the initial position of the ground platform and the local map corresponding to the initial area of ​​the ground platform according to the GNSS data, and complete the positioning initialization of the ground platform by aligning the lidar point cloud data with the local map;

[0028] The time alignment and dedistortion module is used to time-align the lidar point cloud data with the IMU data, recursively extrapolate the time-aligned IMU data, obtain the predicted posture of the ground platform, and use the predicted posture to dedistort the lidar point cloud data;

[0029] The iterative registration module is used to convert the dedistorted LiDAR point cloud data into the global coordinate system according to the predicted pose, and iteratively register the converted LiDAR point cloud data with the local map until the iteration termination condition is met. During the iterative registration process, the state space of the ground platform is updated, and the final estimated pose is output as the real-time pose of the ground platform.

[0030] The map update module is used to calculate the distance between the real-time position of the ground platform and the boundary of the local map according to the real-time position of the ground platform. If the distance is less than a preset distance threshold, the local map corresponding to the area where the ground platform is located is updated to realize autonomous positioning of the ground platform.

[0031] Compared with the prior art, the above-mentioned method and system for autonomous positioning of the ground platform under the guidance of prior information of the UAV has the following beneficial effects:

[0032] 1. Based on the high maneuverability and rich perception capabilities of drones, drones are used to quickly fly to the mission area before the mission begins, and the mission area is efficiently mapped through the sensors they carry. The generated high-precision three-dimensional prior point cloud map and reference benchmark point coordinates are sent back to the ground platform. The ground platform can use the prior map information provided by the drone as a positioning guide, and combine it with its own real-time sensor data to perform ground platform positioning initialization and efficient point cloud registration in turn, thereby improving the accuracy and robustness of the ground platform's autonomous positioning.

[0033] 2. By dividing the prior point cloud map provided by the drone into blocks and encoding the stored regions, the corresponding local map of the area where the ground platform is located can be quickly located through encoding, which greatly improves the speed and accuracy of data processing and analysis. The local map is further divided into blocks and numbered and dynamically updated using a sliding window mechanism, which can ensure a small memory usage while maintaining a faster map update speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of a method for autonomous positioning of a ground platform guided by prior information of a UAV in one embodiment;

[0035] Figure 2 A schematic diagram of a priori point cloud map constructed by a drone in one embodiment;

[0036] Figure 3 A schematic diagram of a map area segmentation result of a priori point cloud map in one embodiment;

[0037] Figure 4 A schematic diagram of updating a local map sliding window in one embodiment;

[0038] Figure 5 A schematic diagram of a real-time positioning result of a ground platform in an embodiment;

[0039] Figure 6 The figure is a schematic diagram of a process of positioning by a ground platform autonomous positioning system guided by prior information of a UAV in one embodiment. DETAILED DESCRIPTION

[0040] 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.

[0041] In one embodiment, Figure 1 As shown, a method for autonomous positioning of a ground platform under the guidance of prior information of a UAV is provided, comprising the following steps:

[0042] Step S1, using the drone to build a priori point cloud map, and transmitting the priori point cloud map and its corresponding reference base point coordinates back to the ground platform for map area segmentation and encoding storage.

[0043] The specific implementation process of step S1 includes:

[0044] First, the multi-dimensional environmental data in the mission area is collected using the perception sensors carried by the drone. The collected data is fused and processed to generate a priori point cloud map. The inertial navigation system (IMU) carried by the drone is used to calibrate the point cloud pose of the priori point cloud map to ensure that the priori point cloud map is aligned to the global coordinate system. The priori point cloud map finally constructed by the drone in the mission area is as follows: Figure 2 shown.

[0045] Then, according to the wireless communication module carried by the UAV, the a priori point cloud map and its corresponding reference benchmark point coordinates are transmitted back to the ground platform in real time. On the ground platform, a grid division algorithm is used to divide the a priori point cloud map into regions, and a unique coding identifier is generated for the local map corresponding to each region based on the reference benchmark point coordinates, thereby realizing the coded storage of each local map.

[0046] It can be understood that the map area division and encoding storage of the prior point cloud map enables the ground platform to quickly load the local map of the specified area, thereby improving the speed and accuracy of subsequent data processing and analysis. Figure 3 As shown, Figure 3 Different color blocks represent different areas.

[0047] Step S2, based on the ground platform's own sensors, obtain the lidar point cloud data, IMU data and GNSS data, obtain the initial position of the ground platform and the local map corresponding to the initial area of ​​the ground platform according to the GNSS data, and complete the ground platform positioning initialization by aligning the lidar point cloud data with the local map.

[0048] The specific implementation process of step S2 includes:

[0049] During operation, the ground platform obtains real-time environmental data through its own sensors, including lidar point cloud data, IMU data, and GNSS (Global Positioning System Signal) data. Each sensor data is timestamped, which is used for time alignment of multi-sensor data. Among them, GNSS data is only used for ground platform positioning initialization, and lidar point cloud data and IMU data are used to provide local odometer constraints.

[0050] The ground platform positioning initialization includes: when the ground platform is started, it obtains the initial position and posture of the ground platform according to the GNSS data received by its own inertial navigation system, obtains the reference coordinates of the ground platform in the prior point cloud map based on the initial position and posture calculation, extracts the local map corresponding to the area where the ground platform is initially located according to the reference coordinates, and uses the point cloud matching algorithm to align the lidar point cloud data with the local map to complete the ground platform positioning initialization.

[0051] Step S3, after time-aligning the lidar point cloud data with the IMU data, recursively extrapolating the time-aligned IMU data to obtain the predicted posture of the ground platform, and using the predicted posture to dedistort the lidar point cloud data.

[0052] The specific implementation process of step S3 includes:

[0053] First, the time alignment is performed according to the timestamps contained in both the LiDAR point cloud data and the IMU data, and the time-aligned IMU data is recursively extrapolated to obtain the predicted position and posture of the ground platform, and the state prior of the ground platform is updated; among them, the state space of the ground platform Expressed as

[0054] ;

[0055] in, is the location of the ground platform, is the speed of the ground platform, is the posture of the ground platform, and are the IMU acceleration and gyroscope bias respectively, is the gravity vector.

[0056] Then, the predicted pose is used to dedistort the LiDAR point cloud data. It is understandable that since the ground platform may move during the LiDAR scanning, the LiDAR point cloud data is prone to distortion. By dedistorting each point through the predicted pose, more accurate LiDAR point cloud data can be corrected.

[0057] Step S4, converting the dedistorted LiDAR point cloud data into the global coordinate system according to the predicted pose, and iteratively aligning the converted LiDAR point cloud data with the local map until the iteration termination condition is met, and in the iterative alignment process, updating the state space of the ground platform, outputting the final estimated pose as the real-time pose of the ground platform.

[0058] The specific implementation process of step S4 includes:

[0059] The dedistorted lidar point cloud data is converted to the global coordinate system according to the predicted pose. The converted lidar point cloud data is registered with the local map using the improved iterative error Kalman filter algorithm to obtain the estimated pose of the ground platform. The coordinates of the lidar point cloud data are converted again according to the estimated pose, and the re-converted lidar point cloud data is iteratively registered with the local map until the registration error is less than the preset error threshold or the number of iterations is greater than the preset iteration threshold. During the iterative registration process, the state space of the ground platform is updated in real time, and the final estimated pose is output as the real-time pose of the ground platform.

[0060] Step S5, calculate the distance between the real-time position of the ground platform and the boundary of the local map according to the real-time position and posture of the ground platform. If the distance is less than a preset distance threshold, update the local map corresponding to the area where the ground platform is located to achieve autonomous positioning of the ground platform.

[0061] Specifically, in order to improve real-time performance and reduce the amount of calculation, each local map uses a sliding window mechanism for dynamic update, such as Figure 4 As shown, each local map is stored in the form of blocks, and each map sub-block obtained by blocking a single local map is numbered according to its global Gaussian coordinates. Figure 4 It can be seen that when the ground platform moves from point A to point B, some map sub-blocks are released (orange), some map sub-blocks are kept (green), and some map sub-blocks are updated (blue). This strategy ensures a smaller memory usage and a faster update speed. In addition, the multi-threaded mode is adopted, and the map update and map matching positioning are processed in parallel to ensure the stable output of the positioning results. The real-time positioning results of the ground platform are as follows Figure 5 shown.

[0062] The local map is dynamically updated using a sliding window mechanism. Specifically, the strategy of updating only three map sub-blocks each time is adopted, including:

[0063] In the ground platform positioning initialization stage, 9 map sub-blocks in the local map corresponding to the initial location of the ground platform are loaded, and the global Gaussian coordinates of the central map sub-block are calculated;

[0064] During the autonomous positioning process of the ground platform, determine the global positioning result of the ground platform at the current moment With the current center map subblock The position relationship, when exist If it is outside the range, according to and The relative position relationship between the ground platform and the center is determined, and the corresponding direction is selected for map update. The three map sub-blocks in the direction opposite to the ground platform are released, and three new map sub-blocks are loaded along the forward direction of the ground platform. At the next moment, the position relationship between the global positioning result of the ground platform and the center map sub-block is continued to be determined. exist When within the bounds, keep the map unchanged.

[0065] The above method uses the prior map information provided by the UAV as a positioning guide, and combines it with the sensor data collected in real time by the ground platform to perform ground platform positioning initialization and efficient point cloud registration in sequence, which can improve the accuracy and robustness of the ground platform's autonomous positioning.

[0066] In one embodiment, a ground platform autonomous positioning system guided by prior information of a drone is provided. The process of the system for autonomous positioning of the ground platform is as follows: Figure 6 As shown, the system includes:

[0067] The prior information acquisition module is used to construct a priori point cloud map using the UAV, and transmit the priori point cloud map and its corresponding reference point coordinates back to the ground platform for map area segmentation and encoding storage;

[0068] The positioning initialization module is used to obtain the lidar point cloud data, IMU data and GNSS data based on the ground platform's own sensors, obtain the initial position of the ground platform and the local map corresponding to the initial area of ​​the ground platform according to the GNSS data, and complete the positioning initialization of the ground platform by aligning the lidar point cloud data with the local map;

[0069] The time alignment and dedistortion module is used to time-align the lidar point cloud data with the IMU data, recursively extrapolate the time-aligned IMU data, obtain the predicted posture of the ground platform, and use the predicted posture to dedistort the lidar point cloud data;

[0070] The iterative registration module is used to convert the dedistorted LiDAR point cloud data into the global coordinate system according to the predicted pose, and iteratively register the converted LiDAR point cloud data with the local map until the iteration termination condition is met. During the iterative registration process, the state space of the ground platform is updated, and the final estimated pose is output as the real-time pose of the ground platform.

[0071] The map update module is used to calculate the distance between the real-time position of the ground platform and the boundary of the local map according to the real-time position of the ground platform. If the distance is less than a preset distance threshold, the local map corresponding to the area where the ground platform is located is updated to realize autonomous positioning of the ground platform.

[0072] For the specific definition of the ground platform autonomous positioning system under the guidance of drone prior information, please refer to the definition of the ground platform autonomous positioning method under the guidance of drone prior information in the above text, which will not be repeated here. Each module in the above-mentioned ground platform autonomous positioning system under the guidance of drone prior information can be implemented in whole or in part through software, hardware and their combination. The above-mentioned 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.

[0073] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above 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.

[0074] 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 understood as limiting the scope of the present 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 present application shall be subject to the attached claims.

Claims

1. A method for autonomous positioning of a ground platform guided by prior information of an unmanned aerial vehicle, characterized in that: The method comprises: Using drones to construct a priori point cloud map, and transmitting the priori point cloud map and its corresponding reference point coordinates back to the ground platform for map area segmentation and encoding storage; Based on the sensors of the ground platform itself, the laser radar point cloud data, IMU data and GNSS data are obtained, and the initial position and posture of the ground platform and the local map corresponding to the initial area of ​​the ground platform are obtained according to the GNSS data, and the positioning initialization of the ground platform is completed by aligning the laser radar point cloud data with the local map; After the laser radar point cloud data and the IMU data are time-aligned, the predicted posture of the ground platform is obtained by recursively performing the time-aligned IMU data, and the laser radar point cloud data is dedistorted using the predicted posture; The dedistorted LiDAR point cloud data is converted to a global coordinate system according to the predicted pose, and the converted LiDAR point cloud data is iteratively registered with the local map until an iteration termination condition is met, and during the iterative registration process, the state space of the ground platform is updated, and the final estimated pose is output as the real-time pose of the ground platform; The distance between the real-time position of the ground platform and the boundary of the local map is calculated based on the real-time position of the ground platform. If the distance is less than the preset distance threshold, the local map corresponding to the area where the ground platform is located is updated to achieve autonomous positioning of the ground platform.

2. The method according to claim 1, characterized in that Use drones to build a priori point cloud map, and transmit the priori point cloud map and its corresponding reference point coordinates back to the ground platform for map area segmentation and encoding storage, including: The multi-dimensional environmental data in the mission area is collected by the perception sensor carried by the drone, and the a priori point cloud map is generated by fusing the collected data. The point cloud pose of the a priori point cloud map is calibrated by the inertial navigation system carried by the drone to ensure that the a priori point cloud map is aligned to the global coordinate system. According to the wireless communication module carried by the UAV, the a priori point cloud map and its corresponding reference reference point coordinates are transmitted back to the ground platform in real time. On the ground platform side, a grid division algorithm is used to divide the a priori point cloud map into regions, and a unique coding identifier is generated for the local map corresponding to each region based on the reference reference point coordinates, thereby realizing the coded storage of each local map.

3. The method according to claim 1, characterized in that Acquire the initial position of the ground platform and the local map corresponding to the area where the ground platform is initially located according to the GNSS data, and complete the initialization of the ground platform positioning by aligning the laser radar point cloud data with the local map, including: The initial posture of the ground platform is obtained according to the GNSS data, and the reference coordinates of the ground platform in the prior point cloud map are calculated based on the initial posture. The local map corresponding to the area where the ground platform is initially located is extracted according to the reference coordinates, and the lidar point cloud data is aligned with the local map using a point cloud matching algorithm to complete the initialization of the ground platform positioning.

4. The method according to claim 1, characterized in that: After the laser radar point cloud data and the IMU data are time-aligned, the predicted position and posture of the ground platform are obtained by recursively performing the time-aligned IMU data, including: Time alignment is performed according to the timestamps contained in the laser radar point cloud data and the IMU data, and the time-aligned IMU data is recursively extrapolated to obtain the predicted position and posture of the ground platform, and the state prior of the ground platform is updated; wherein the state space of the ground platform Expressed as ； in, is the location of the ground platform, is the speed of the ground platform, is the posture of the ground platform, and are the IMU acceleration and gyroscope bias respectively, is the gravity vector.

5. The method according to claim 4, characterized in that The dedistorted LiDAR point cloud data is converted to a global coordinate system according to the predicted pose, and the converted LiDAR point cloud data is iteratively registered with the local map until an iteration termination condition is met. During the iterative registration process, the state space of the ground platform is updated, and the final estimated pose is output as the real-time pose of the ground platform, including: According to the predicted pose, the dedistorted lidar point cloud data is converted to the global coordinate system, and the converted lidar point cloud data is iteratively aligned with the local map using an improved iterative error Kalman filter algorithm until the alignment error is less than a preset threshold or the number of iterations exceeds a preset maximum value. During the iterative alignment process, the state space of the ground platform is updated in real time, and the final estimated pose is output as the real-time pose of the ground platform.

6. The method according to claim 1, characterized in that The local map is dynamically updated by using a sliding window mechanism, and each local map is stored in a block form, and each map sub-block obtained by blocking a single local map is numbered according to its global Gaussian coordinates.

7. The method according to claim 6, characterized in that The local map is dynamically updated using a sliding window mechanism, including: In the ground platform positioning initialization stage, 9 map sub-blocks in the local map corresponding to the initial location of the ground platform are loaded, and the global Gaussian coordinates of the central map sub-block are calculated; During the autonomous positioning process of the ground platform, determine the global positioning result of the ground platform at the current moment With the current center map subblock The position relationship, when exist If it is outside the range, according to and The relative position relationship between the ground platform and the center is determined, and the corresponding direction is selected for map update. The three map sub-blocks in the direction opposite to the ground platform are released, and three new map sub-blocks are loaded along the forward direction of the ground platform. At the next moment, the position relationship between the global positioning result of the ground platform and the center map sub-block is continued to be determined. exist When within the bounds, keep the map unchanged.

8. A ground platform autonomous positioning system guided by prior information of unmanned aerial vehicles, characterized in that: The system comprises: A priori information acquisition module is used to construct a priori point cloud map using a drone, and transmit the priori point cloud map and its corresponding reference reference point coordinates back to the ground platform for map area segmentation and encoding storage; The positioning initialization module is used to obtain the laser radar point cloud data, IMU data and GNSS data based on the sensors of the ground platform itself, obtain the initial position of the ground platform and the local map corresponding to the initial area of ​​the ground platform according to the GNSS data, and complete the positioning initialization of the ground platform by aligning the laser radar point cloud data with the local map; A time alignment and dedistortion module, used to time-align the laser radar point cloud data with the IMU data, recursively obtain the predicted posture of the ground platform by performing time alignment on the IMU data, and dedistort the laser radar point cloud data using the predicted posture; An iterative registration module is used to convert the dedistorted LiDAR point cloud data into a global coordinate system according to the predicted pose, and iteratively register the converted LiDAR point cloud data with the local map until an iteration termination condition is met, and during the iterative registration process, update the state space of the ground platform and output the final estimated pose as the real-time pose of the ground platform; The map update module is used to calculate the distance between the real-time position of the ground platform and the boundary of the local map according to the real-time position of the ground platform. If the distance is less than a preset distance threshold, the local map corresponding to the area where the ground platform is located is updated to realize autonomous positioning of the ground platform.

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