A Point Cloud Data Repair Method and System

Abstract

The present invention provides a point cloud data repair method and system. The method includes: obtaining the combined navigation trajectory of a mobile measurement system, and detecting the trajectory mutation regions in the combined navigation trajectory; matching corresponding distorted point cloud segments from the point cloud data to obtain a distorted point cloud trajectory; and updating and optimizing the distorted point cloud trajectory. The present invention repairs the jumped or distorted point cloud data, can obtain point cloud data with accurate positions, and also avoids the re-acquisition of point cloud data.

Description

Technical Field

[0001] The present invention relates to the field of map making, and more specifically, to a method and system for repairing point cloud data. Background Art

[0002] The combined navigation trajectory has mutations caused by chaotic return electromagnetic waves received by the combined navigation device, human non-standard operations (such as reversing resulting in incorrect recording serial numbers of inertial navigation data), atmospheric factors, or satellite quality. Mutations have two characteristics: uncertainty and irregular variability. The mutation of the combined navigation trajectory causes the inaccurate position of the point cloud calculated based on the pos, there are jumps or distortions between adjacent point cloud circles, the smoothness of the point cloud is lost, and problems of point cloud distortion and jump occur. Summary of the Invention

[0003] The present invention provides a method and system for repairing point cloud data in view of the technical problems existing in the prior art.

[0004] According to a first aspect of the present invention, there is provided a method for repairing point cloud data, including:

[0005] Obtaining the combined navigation trajectory of the mobile measurement system and detecting the trajectory mutation area in the combined navigation trajectory;

[0006] Based on the GPStime field of the trajectory mutation area, matching the corresponding point cloud segment, and the corresponding point cloud segment is a distorted point cloud segment;

[0007] Performing trajectory point matching on the original combined navigation trajectory of the trajectory mutation area and the distorted point cloud segment to obtain an initial distorted point cloud trajectory;

[0008] Based on the speed, time interval, and frame distance increment of each frame of point cloud trajectory points of the initial distorted point cloud trajectory, updating the initial distorted point cloud trajectory, and re-solving the point cloud to obtain the distorted point cloud trajectory after the first update;

[0009] Based on the distorted point cloud trajectory after the first update, extracting the feature information of each frame of point cloud trajectory points, performing matching based on the feature information of adjacent two frames of point cloud trajectory points to obtain the frame-to-frame matching optimization parameters, and based on the frame-to-frame matching optimization parameters, updating the distorted point cloud trajectory after the first update to obtain the distorted point cloud trajectory after the second update;

[0010] Based on the distorted point cloud trajectory after the second update, calculating the error distribution value, and according to the error distribution value, optimizing the distorted point cloud trajectory after the second update to obtain the finally optimized point cloud trajectory.

[0011] According to a second aspect of the present invention, there is provided a system for repairing point cloud data, including:

[0012] A detection module, configured to detect a trajectory mutation region in the combined navigation trajectory based on the acquired combined navigation trajectory of the mobile measurement system;

[0013] A matching module, configured to match corresponding point cloud segments based on the GPStime field of the trajectory mutation region, where the corresponding point cloud segments are distorted point cloud segments; and perform trajectory point matching between the original combined navigation trajectory of the trajectory mutation region and the distorted point cloud segments to obtain an initial distorted point cloud trajectory;

[0014] A first update module, configured to update the initial distorted point cloud trajectory based on the speed, time interval, and inter-frame distance increment of each frame of point cloud trajectory points of the initial distorted point cloud trajectory, and re-solve the point cloud to obtain a distorted point cloud trajectory after the first update;

[0015] A second update module, configured to extract feature information of each frame of point cloud trajectory points based on the distorted point cloud trajectory after the first update, perform matching based on the feature information of adjacent two frames of point cloud trajectory points to obtain an inter-frame matching optimization parameter, and update the distorted point cloud trajectory after the first update based on the inter-frame matching optimization parameter to obtain a distorted point cloud trajectory after the second update;

[0016] A third update module, configured to calculate an error allocation value based on the distorted point cloud trajectory after the second update, and optimize the distorted point cloud trajectory after the second update according to the error allocation value to obtain a finally optimized point cloud trajectory.

[0017] According to a third aspect of the present invention, there is provided an electronic device, including a memory and a processor, where the processor is configured to implement the steps of the point cloud data repair method when executing a computer management program stored in the memory.

[0018] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium, on which a computer management program is stored, and the computer management program is configured to implement the steps of the point cloud data repair method when executed by a processor.

[0019] A point cloud data repair method and system provided by the present invention detect a trajectory mutation region in a combined navigation trajectory; match corresponding distorted point cloud segments from point cloud data to obtain a distorted point cloud trajectory; and update and optimize the distorted point cloud trajectory. The present invention repairs jumpy or distorted point cloud data, can obtain point cloud data with accurate positions, and also avoids re-acquisition of point cloud data. Description of the Drawings

[0020] Figure 1 It is a flowchart of a point cloud data repair method provided by the present invention;

[0021] Figure 2Schematic diagram of the process for optimizing the trajectory by point cloud frame - to - frame matching;

[0022] Figure 3 Schematic diagram of the overall process of the point cloud data repair method;

[0023] Figure 4 Schematic diagram of the structure of a point cloud data repair system provided by the present invention;

[0024] Figure 5 Schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0025] Figure 6 Schematic diagram of the hardware structure of a possible computer - readable storage medium provided by the present invention. Specific embodiments

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention. In addition, the technical features in each embodiment or a single embodiment provided by the present invention can be combined with each other arbitrarily to form a feasible technical solution. This combination is not restricted by the order of steps and / or the pattern of structural composition, but must be based on what can be achieved by those of ordinary skill in the art. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0027] Figure 1 Flowchart of a point cloud data repair method provided by the present invention, as Figure 1 shown, the method includes:

[0028] S1, obtaining the combined navigation trajectory of the mobile measurement system and detecting the trajectory mutation regions in the combined navigation trajectory.

[0029] It can be understood that the combined navigation trajectory file of the mobile measurement system is read. The combined navigation trajectory file is complete and includes multiple fields: trajectory line number SeqNum, timestamp GPSTime, latitude Latitude, longitude Longitude, ellipsoidal height H - Ell, roll angle Roll, pitch angle Pitch, heading angle heading, east - direction speed vEast, north - direction speed vNorth, and up - direction speed vUp.

[0030] After obtaining the combined navigation trajectory file of the mobile measurement system, detect the trajectory mutation area in the combined navigation trajectory, specifically including: converting the longitude and latitude coordinates of each trajectory point in the combined navigation trajectory into UTM coordinates; based on the set trajectory thinning threshold d, in meters, extract trajectory points every d meters for the combined navigation trajectory, and obtain the thinned navigation trajectory.

[0031] Based on the thinned navigation trajectory, calculate the coordinate differences (dX, dY, dZ) between two adjacent trajectory points, where dX, dY, and dZ respectively represent the lateral coordinate difference, longitudinal coordinate difference, and elevation coordinate difference between the two trajectory points.

[0032] Set the trajectory mutation threshold s, and judge the relationship between the coordinate difference between two adjacent front and back points of the trajectory and the trajectory mutation threshold s; if dX or dY is greater than s, it is judged as a planar mutation; if dz is greater than the jump threshold s, it is judged as an elevation mutation.

[0033] Detect the trajectory mutation area in the combined navigation trajectory according to the above method.

[0034] S2, based on the GPStime field of the trajectory mutation area, match the corresponding point cloud segment, and the corresponding point cloud segment is a distorted point cloud segment.

[0035] It can be understood that in the above step S1, the trajectory mutation area in the combined navigation trajectory is detected. According to the GPStime field of the selected trajectory mutation area, the corresponding point cloud LAZ segment is matched, and these LAZ point clouds are distorted point clouds.

[0036] S3, match the original combined navigation trajectory of the trajectory mutation area with the distorted point cloud segment to obtain the initial distorted point cloud trajectory.

[0037] It can be understood that in step S2, the distorted point cloud data segment in the point cloud data is found, and the point cloud trajectory of the distorted point cloud segment needs to be obtained according to the trajectory points of the trajectory mutation area.

[0038] Specifically, matching the original combined navigation trajectory of the trajectory mutation area with the distorted point cloud segment to obtain an initial distorted point cloud trajectory, including: obtaining the minimum timestamp minTime and the maximum timestamp maxTime in all distorted point cloud segments to form a time period (minTime, maxTime), and obtaining the start and end timestamps of each frame of point cloud according to the frame number field of the point cloud; extracting the effective combined navigation trajectory of this time period from the original combined navigation trajectory according to the time period (minTime, maxTime); using the time nearest neighbor matching method to match trajectory points according to the start time of each frame of point cloud in the distorted point cloud segment, matching one time nearest neighbor trajectory point for each frame of point cloud, and obtaining the matched point cloud trajectory points, and these point cloud trajectory points form the initial distorted point cloud trajectory L0.

[0039] For the Roll, Pitch, heading, vEast, vNorth, vUp fields of the distorted point cloud trajectory, convert the three attitude angles of Roll, Pitch, heading and the velocities in the northeast-down (vEast, vNorth, vUp) coordinate system to the right-front-up coordinate system of the vehicle body system, with the right as the x-axis, the front as the y-axis, and the up as the z-axis.

[0040] According to the y-axis velocity of the vehicle body system, for velocities less than or equal to 0, it can be determined that the vehicle is parking or reversing at this moment, and mark these point cloud frames as parking or reversing point clouds.

[0041] S4. Based on the velocity, time interval and inter-frame distance increment of each frame of point cloud trajectory points of the initial distorted point cloud trajectory, update the initial distorted point cloud trajectory, and re-solve the point cloud to obtain the distorted point cloud trajectory after the first update.

[0042] It can be understood that for the initial distorted point cloud trajectory, it needs to be updated and optimized. Specifically, according to the northeast-down velocities of each frame of point cloud trajectory points of the initial distorted point cloud trajectory, the time interval between every two adjacent frames of point clouds, and the inter-frame distance increment of the northeast-down of every two adjacent frames of point clouds, taking the trajectory point of the starting frame as the starting point, integrating the inter-frame distance increment of every two adjacent frames of point clouds, and updating the initial distorted point cloud trajectory.

[0043] Suppose the starting point is A, the distance between two adjacent frames is Di (i = 1, 2,..., n - 1), the trajectory point of the second frame is A + D1, the trajectory point of the third frame is A + D1 + D2, and so on, the trajectory point of the nth frame is A + D1 + D2 +... + Dn - 1; finally, a new trajectory is obtained, and the new trajectory replaces the old trajectory.

[0044] Based on the updated initial distorted point cloud trajectory, re-solve the point cloud. Subtract the coordinates of the original trajectory points in the initial distorted point cloud trajectory from the coordinates of each frame of the point cloud, and then add the coordinates of the trajectory points in the updated initial distorted point cloud trajectory to obtain the first updated distorted point cloud trajectory.

[0045] S5. Based on the first updated distorted point cloud trajectory, extract the feature information of each frame of the point cloud trajectory points. Match based on the feature information of the adjacent two frames of the point cloud trajectory points to obtain the inter-frame matching optimization parameters. Based on the inter-frame matching optimization parameters, update the first updated distorted point cloud trajectory to obtain the second updated distorted point cloud trajectory.

[0046] It can be understood that reference can be made to Figure 2 , for the distorted point cloud trajectory after the first update and optimization, extract the features of each frame of the point cloud trajectory points. Specifically, use fast corner detection to extract corners, and use intensity information binarization to extract the lane lines of a single frame of the point cloud; for the front and rear adjacent frames of the point cloud, match the same-name corners and lane lines to obtain the inter-frame matching optimization parameters, that is, the correction value of the inter-frame distance increment; correct the original inter-frame distance increment value according to the correction value of the inter-frame distance increment, and then update the first updated distorted point cloud trajectory to obtain the second updated distorted point cloud trajectory.

[0047] S6. Based on the second updated distorted point cloud trajectory, calculate the error allocation value. According to the error allocation value, optimize the second updated distorted point cloud trajectory to obtain the finally optimized point cloud trajectory.

[0048] It can be understood that for the second updated distorted point cloud trajectory, optimize it again. Specifically, obtain the coordinates of the tail point of the trajectory of the second updated distorted point cloud trajectory, match the original trajectory points in the initial distorted point cloud trajectory through the time stamp of the coordinates of the tail point of the trajectory, and calculate the position difference (dX1, dY1, dZ1) between the two points; set the error allocation threshold c, such as 0.01, and calculate the maximum number of trajectory points N = max(|dX1 / c|, |dY1 / c|, |dZ1 / c|) that meet the error allocation threshold according to the position difference (dX1, dY1, dZ1), that is, the number of trajectory points with a position difference less than the error allocation threshold; calculate the error allocation value D = (dX1 / N, dY1 / N, dZ1 / N), and allocate the error to the first updated distorted point cloud trajectory starting from the edge connection point to obtain the finally optimized point cloud trajectory.

[0049] For example, taking the edge - connecting point as the starting point A1, select N - 1 trajectory points further according to the time of the trajectory. The error distribution for the first trajectory point A1 = A1+D; the second trajectory point A2 = A2+(N - 1) / N*D; the i - th trajectory point Ai = Ai+(N - i + 1) / N*D. In this way, each trajectory point is updated and optimized.

[0050] Finally, based on the updated and optimized point - cloud trajectory, construct a trajectory - point model based on the cubic B - spline function; based on the finally optimized point - cloud trajectory and the set of point - cloud frames in the vehicle body system, re - transform it to the UTM coordinate system to obtain the corrected and repaired point - cloud data.

[0051] See Figure 3 , the overall flowchart of the point - cloud data repair method provided by the present invention mainly includes the following steps:

[0052] 1. Read the combined navigation trajectory and point - cloud data of the mobile measurement system, detect the trajectory mutation region from the combined navigation trajectory, and obtain the valid trajectory of the trajectory mutation region;

[0053] 2. Find the distorted point - cloud segment corresponding to the trajectory mutation region in the point - cloud data, and obtain the corresponding initial distorted point - cloud trajectory;

[0054] 3. Use different methods to update and optimize the initial distorted point - cloud trajectory to obtain the finally optimized point - cloud data.

[0055] 4. Based on the finally optimized point - cloud data, construct a trajectory model and perform coordinate transformation to obtain the corrected and repaired point - cloud data.

[0056] Figure 4 The structure diagram of a point - cloud data repair system provided by an embodiment of the present invention includes a detection module 401, a matching module 402, a first update module 403, a second update module 404, and a third update module 405, where:

[0057] The detection module 401 is used to detect the trajectory mutation region in the combined navigation trajectory based on the obtained combined navigation trajectory of the mobile measurement system;

[0058] The matching module 402 is used to match the corresponding point - cloud segment based on the GPStime field of the trajectory mutation region, where the corresponding point - cloud segment is a distorted point - cloud segment; and perform trajectory - point matching between the original combined navigation trajectory of the trajectory mutation region and the distorted point - cloud segment to obtain the initial distorted point - cloud trajectory;

[0059] The first update module 403 is configured to update the initial distorted point cloud trajectory based on the velocity, time interval, and inter-frame distance increment of each point cloud trajectory point in the initial distorted point cloud trajectory, and re-solve the point cloud to obtain the distorted point cloud trajectory after the first update;

[0060] The second update module 404 is configured to extract the feature information of each point cloud trajectory point based on the distorted point cloud trajectory after the first update, perform matching based on the feature information of adjacent two-frame point cloud trajectory points to obtain the inter-frame matching optimization parameters, and update the distorted point cloud trajectory after the first update based on the inter-frame matching optimization parameters to obtain the distorted point cloud trajectory after the second update;

[0061] The third update module 405 is configured to calculate the error allocation value based on the distorted point cloud trajectory after the second update, and optimize the distorted point cloud trajectory after the second update according to the error allocation value to obtain the finally optimized point cloud trajectory.

[0062] It can be understood that a point cloud data repair system provided by the present invention corresponds to the point cloud data repair methods provided in the foregoing embodiments. The relevant technical features of the point cloud data repair system can refer to the relevant technical features of the point cloud data repair method, which will not be elaborated herein.

[0063] Please refer to Figure 5 , Figure 5 which is a schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. As Figure 5 shown, an embodiment of the present invention provides an electronic device, including a memory 510, a processor 520, and a computer program 511 stored on the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 511, the steps of the point cloud data repair method are implemented.

[0064] Please refer to Figure 6 , Figure 6 which is a schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. As Figure 6 shown, this embodiment provides a computer-readable storage medium 600, on which a computer program 611 is stored. When the computer program 611 is executed by a processor, the steps of the point cloud data repair method are implemented.

[0065] A point cloud data repair method and system provided by an embodiment of the present invention repair the jumped or distorted point cloud data, and can obtain the point cloud data with accurate positions and smooth point cloud data, solve the problems of point cloud distortion and jump, and there is no need to re-collect the point cloud data.

[0066] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0067] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0068] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0069] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0071] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0072] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for repairing point cloud data, characterized in that, Including: Obtain the integrated navigation trajectory of the mobile measurement system, and detect the trajectory mutation regions in the integrated navigation trajectory; Based on the GPStime timestamp field of the trajectory mutation regions, match the corresponding point cloud segments, and the corresponding point cloud segments are distorted point cloud segments; Perform trajectory point matching between the original integrated navigation trajectory of the trajectory mutation regions and the distorted point cloud segments to obtain an initial distorted point cloud trajectory; Based on the speed, time interval, and frame - to - frame distance increment of each frame of point cloud trajectory points of the initial distorted point cloud trajectory, update the initial distorted point cloud trajectory, and re - calculate the point cloud to obtain the distorted point cloud trajectory after the first update; Based on the distorted point cloud trajectory after the first update, extract the feature information of each frame of point cloud trajectory points, perform matching based on the feature information of adjacent two - frame point cloud trajectory points to obtain the inter - frame matching optimization parameters, and based on the inter - frame matching optimization parameters, update the distorted point cloud trajectory after the first update to obtain the distorted point cloud trajectory after the second update; Based on the distorted point cloud trajectory after the second update, calculate the error allocation value, and according to the error allocation value, optimize the distorted point cloud trajectory after the second update to obtain the finally optimized point cloud trajectory; The calculating the error allocation value based on the distorted point cloud trajectory after the second update, and optimizing the distorted point cloud trajectory after the second update according to the error allocation value to obtain the finally optimized point cloud trajectory includes: Obtain the coordinate of the tail point of the trajectory of the distorted point cloud trajectory after the second update, match the original trajectory points in the initial distorted point cloud trajectory through the timestamp of the coordinate of the tail point of the trajectory, and calculate the position difference (dX1, dY1, dZ1) between the two points; Set an error allocation threshold c, and according to the position difference (dX1, dY1, dZ1), calculate the maximum number of trajectory points N that meet the error allocation threshold N = max(|dX1 / c|, |dY1 / c|, |dZ1 / c|); Calculate the error allocation value (dX1 / N, dY1 / N, dZ1 / N), and starting from the edge - connecting point as the error allocation starting point, allocate the error to the distorted point cloud trajectory after the first update to obtain the finally optimized point cloud trajectory.

2. The method for repairing point cloud data according to claim 1, characterized in that, The detecting the trajectory mutation regions in the integrated navigation trajectory includes: Convert the longitude and latitude coordinates of each trajectory point in the integrated navigation trajectory into utm coordinates; Based on a set trajectory thinning threshold d, extract trajectory points every d intervals for the integrated navigation trajectory to obtain the thinned navigation trajectory; Based on the thinned navigation trajectory, calculate the coordinate differences (dX, dY, dZ) between adjacent two trajectory points, where dX, dY, and dZ respectively represent the lateral coordinate difference, longitudinal coordinate difference, and elevation coordinate difference between two trajectory points; Set a trajectory mutation threshold s, and judge the relationship between the coordinate difference between adjacent front and back two points of the trajectory and the trajectory mutation threshold s; if dX or dY is greater than s, it is judged as a planar mutation; if dz is greater than the jump threshold s, it is judged as an elevation mutation.

3. The method for repairing point cloud data according to claim 1, characterized in that, Performing trajectory point matching between the original integrated navigation trajectory of the trajectory mutation regions and the distorted point cloud segments to obtain an initial distorted point cloud trajectory includes: Obtain the minimum timestamp minTime and the maximum timestamp maxTime in all the distorted point cloud segments to form a time period (minTime, maxTime), and based on the frame number field of the point cloud, obtain the start and end timestamps of each frame of the point cloud; Extract the valid combined navigation trajectory of this time period from the original combined navigation trajectory according to the time period (minTime, maxTime); According to the start time of each frame of the point cloud in the distorted point cloud segment, use the time nearest neighbor matching method to match the trajectory points, and match one time nearest neighbor trajectory point for each frame of the point cloud to obtain the initial distorted point cloud trajectory L0.

4. The method for repairing point cloud data according to claim 1 or 3, characterized in that, Updating the initial distorted point cloud trajectory based on the speed, time interval, and inter-frame distance increment of the trajectory points of each frame of the point cloud in the initial distorted point cloud trajectory, and re-solving the point cloud to obtain the distorted point cloud trajectory after the first update, including: According to the north-east-down speed of the trajectory points of each frame of the point cloud in the initial distorted point cloud trajectory, the time interval between every two adjacent frames of the point cloud, and the north-east-down inter-frame distance increment between every two adjacent frames of the point cloud, using the trajectory point of the starting frame as the starting point, and integrating with the inter-frame distance increment between every two adjacent frames of the point cloud to update the initial distorted point cloud trajectory; Based on the updated initial distorted point cloud trajectory, re-solve the point cloud, subtract the original trajectory point coordinates in the initial distorted point cloud trajectory from the coordinates of each frame of the point cloud, and then add the trajectory point coordinates in the updated initial distorted point cloud trajectory to obtain the distorted point cloud trajectory after the first update.

5. The method for repairing point cloud data according to claim 4, characterized in that, Extracting the feature information of the trajectory points of each frame of the point cloud based on the distorted point cloud trajectory after the first update, matching based on the feature information of the trajectory points of two adjacent frames of the point cloud to obtain the inter-frame matching optimization parameters, and updating the distorted point cloud trajectory after the first update based on the inter-frame matching optimization parameters to obtain the distorted point cloud trajectory after the second update, including: Based on each frame of the point cloud in the distorted point cloud trajectory after the first update, use the fast corner detection to extract the corner points, and use the intensity information binarization to extract the lane lines of the single-frame point cloud; For the front and rear adjacent frames of the point cloud, match the same-name corner points and lane lines to obtain the inter-frame matching optimization parameters, that is, the correction value of the inter-frame distance increment; Update the distorted point cloud trajectory after the first update according to the correction value of the inter-frame distance increment to obtain the distorted point cloud trajectory after the second update.

6. The method for repairing point cloud data according to any one of claims 1 or 2, characterized in that, After obtaining the finally optimized point cloud trajectory, it further includes: Construct a trajectory point model based on the finally optimized point cloud trajectory using the cubic B-spline function; Based on the finally optimized point cloud trajectory and the set of point cloud frames in the vehicle body coordinate system, convert to the UTM coordinate system to obtain the corrected point cloud data.

7. A system for repairing point cloud data, characterized in that, Including: A detection module for detecting the trajectory mutation area in the combined navigation trajectory based on the obtained combined navigation trajectory of the mobile measurement system; A matching module for matching the corresponding point cloud segment based on the GPStime field of the trajectory mutation area, where the corresponding point cloud segment is the distorted point cloud segment; and performing trajectory point matching between the original combined navigation trajectory of the trajectory mutation area and the distorted point cloud segment to obtain the initial distorted point cloud trajectory; A first update module, configured to update the initial warped point cloud trajectory based on the velocity, time interval, and inter-frame distance increment of each point cloud trajectory point of the initial warped point cloud trajectory, and re-solve the point cloud to obtain the warped point cloud trajectory after the first update; A second update module, configured to extract the feature information of each point cloud trajectory point based on the warped point cloud trajectory after the first update, perform matching based on the feature information of adjacent two-frame point cloud trajectory points to obtain the inter-frame matching optimization parameters, and update the warped point cloud trajectory after the first update based on the inter-frame matching optimization parameters to obtain the warped point cloud trajectory after the second update; A third update module, configured to calculate an error allocation value based on the warped point cloud trajectory after the second update, and optimize the warped point cloud trajectory after the second update according to the error allocation value to obtain the finally optimized point cloud trajectory; The calculating an error allocation value based on the warped point cloud trajectory after the second update, and optimizing the warped point cloud trajectory after the second update according to the error allocation value to obtain the finally optimized point cloud trajectory includes: Obtaining the coordinate of the tail point of the warped point cloud trajectory after the second update, matching the original trajectory point in the initial warped point cloud trajectory through the timestamp of the coordinate of the tail point of the trajectory, and calculating the position difference (dX1, dY1, dZ1) between the two points; Setting an error allocation threshold c, and calculating the maximum number of trajectory points N = max(|dX1 / c|, |dY1 / c|, |dZ1 / c|) that meet the error allocation threshold according to the position difference (dX1, dY1, dZ1); Calculating the error allocation value (dX1 / N, dY1 / N, dZ1 / N), and allocating the error to the warped point cloud trajectory after the first update starting from the edge connection point to obtain the finally optimized point cloud trajectory.

8. An electronic device, characterized in that, Including a memory and a processor, the processor is configured to implement the steps of the point cloud data repair method according to any one of claims 1-6 when executing a computer management program stored in the memory.

9. A computer-readable storage medium, characterized in that, Stored thereon is a computer management program, and the computer management program is configured to implement the steps of the point cloud data repair method according to any one of claims 1-6 when executed by the processor.

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