CN116067381A - Point cloud data restoration method and system - Google Patents
Point cloud data restoration method and system Download PDFInfo
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- CN116067381A CN116067381A CN202211715039.2A CN202211715039A CN116067381A CN 116067381 A CN116067381 A CN 116067381A CN 202211715039 A CN202211715039 A CN 202211715039A CN 116067381 A CN116067381 A CN 116067381A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3833—Creation or updating of map data characterised by the source of data
- G01C21/3841—Data obtained from two or more sources, e.g. probe vehicles
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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Abstract
The invention provides a point cloud data restoration method and a system, wherein the method comprises the following steps: acquiring an integrated navigation track of a mobile measurement system and detecting a track mutation area in the integrated navigation track; matching corresponding distorted point cloud segments from the point cloud data to obtain distorted point cloud tracks; and updating and optimizing the distorted point cloud track. The invention repairs the jumping or distorted point cloud data, can obtain the point cloud data with accurate position, and also avoids the re-acquisition of the point cloud data.
Description
Technical Field
The invention relates to the field of map making, in particular to a point cloud data restoration method and system.
Background
The integrated navigation equipment receives the electromagnetic wave confusion returned, artificial irregular operation (such as error of inertial navigation data record serial number caused by reversing), and mutation of the integrated navigation track caused by atmospheric factors or satellite quality and the like. Mutations have two characteristics: uncertainty, irregular variability. The point cloud positions calculated according to pos are inaccurate due to mutation of the combined navigation track, jump or distortion exists between adjacent point cloud circles, smoothness of the point cloud is lost, and the point cloud distortion and jump are caused.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a point cloud data restoration method and a point cloud data restoration system.
According to a first aspect of the present invention, there is provided a point cloud data restoration method, including:
acquiring an integrated navigation track of a mobile measurement system and detecting a track mutation area in the integrated navigation track;
matching corresponding point cloud segments based on GPStime fields of the track mutation areas, wherein the corresponding point cloud segments are distorted point cloud segments;
performing track point matching on the original combined navigation track of the track mutation region and the twisted point cloud segment to obtain an initial twisted point cloud track;
updating the initial distorted point cloud track based on the speed, the time interval and the inter-frame distance increment of each frame point cloud track point of the initial distorted point cloud track, and recalculating the point cloud to obtain a distorted point cloud track after the first updating;
extracting characteristic information of each frame of point cloud track points based on the first updated distorted point cloud track, matching based on the characteristic information of two adjacent frames of point cloud track points, obtaining inter-frame matching optimization parameters, updating the first updated distorted point cloud track based on the inter-frame matching optimization parameters, and obtaining a second updated distorted point cloud track;
and calculating an error distribution value based on the twisted point cloud track updated for the second time, and optimizing the twisted point cloud track updated for the second time according to the error distribution value to obtain a final optimized point cloud track.
According to a second aspect of the present invention, there is provided a point cloud data repair system comprising:
the detection module is used for detecting a track mutation area in the integrated navigation track based on the acquired integrated navigation track of the mobile measurement system;
the matching module is used for matching corresponding point cloud segments based on the GPStime field of the track mutation area, wherein the corresponding point cloud segments are distorted point cloud segments; performing track point matching on the original combined navigation track of the track mutation region and the distorted point cloud segment to obtain an initial distorted point cloud track;
the first updating module is used for updating the initial distorted point cloud track based on the speed, the time interval and the inter-frame distance increment of each frame point cloud track point of the initial distorted point cloud track, and re-calculating the point cloud to obtain the distorted point cloud track after the first updating;
the second updating module is used for extracting characteristic information of each frame of point cloud track points based on the first updated distorted point cloud track, matching the characteristic information of every two adjacent frames of point cloud track points to obtain inter-frame matching optimization parameters, updating the first updated distorted point cloud track based on the inter-frame matching optimization parameters, and obtaining a second updated distorted point cloud track;
and the third updating module is used for calculating an error distribution value based on the twisted point cloud track after the second updating, optimizing the twisted point cloud track after the second updating according to the error distribution value, and obtaining the finally optimized point cloud track.
According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor for implementing the steps of a point cloud data repair method when executing a computer management class program stored in the memory.
According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer management class program which, when executed by a processor, implements the steps of a point cloud data restoration method.
The invention provides a point cloud data restoration method and a point cloud data restoration system, wherein a track mutation area in an integrated navigation track is detected; matching corresponding distorted point cloud segments from the point cloud data to obtain distorted point cloud tracks; and updating and optimizing the distorted point cloud track. The invention repairs the jumping or distorted point cloud data, can obtain the point cloud data with accurate position, and also avoids the re-acquisition of the point cloud data.
Drawings
FIG. 1 is a flow chart of a point cloud data restoration method provided by the invention;
FIG. 2 is a schematic flow chart of a point cloud inter-frame matching optimization trajectory;
FIG. 3 is a schematic overall flow diagram of a point cloud data repair method;
fig. 4 is a schematic structural diagram of a point cloud data repair system provided by the invention;
fig. 5 is a schematic hardware structure of one possible electronic device according to the present invention;
fig. 6 is a schematic hardware structure of a possible computer readable storage medium according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of 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, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. In addition, the technical features of each embodiment or the single embodiment provided by the invention can be combined with each other at will to form a feasible technical scheme, and the combination is not limited by the sequence of steps and/or the structural composition mode, but is necessarily based on the fact that a person of ordinary skill in the art can realize the combination, and when the technical scheme is contradictory or can not realize, the combination of the technical scheme is not considered to exist and is not within the protection scope of the invention claimed.
Fig. 1 is a flowchart of a point cloud data restoration method provided by the present invention, where, as shown in fig. 1, the method includes:
s1, acquiring an integrated navigation track of a mobile measurement system, and detecting a track mutation area in the integrated navigation track.
It can be understood that, the integrated navigation track file of the mobile measurement system is read, and the integrated navigation track file is complete in record and comprises a plurality of fields: track line number SeqNum, timestamp GPSTime, latitude, longitude, earth height H-Ell, roll angle Roll, pitch angle, heading angle heading, eastern direction speed vEast, northern direction speed vnath, and sky direction speed vUp.
Detecting a track mutation area in the integrated navigation track based on the acquired integrated navigation track file of the mobile measurement system, wherein the method specifically comprises the following steps: converting longitude and latitude coordinates of each track point in the integrated navigation track into utm coordinates; and extracting track points from the combined navigation tracks at intervals of d meters based on a set track thinning threshold d in meters, and obtaining the navigation tracks after thinning.
And calculating coordinate differences (dX, dY, dZ) between two adjacent track points based on the navigation track after the thinning treatment, wherein dX, dY and dZ respectively represent a transverse coordinate difference, a longitudinal coordinate difference and an elevation coordinate difference between the two track points.
Setting a track mutation threshold value s, and judging the relation between the coordinate difference value between the two adjacent points of the track and the track mutation threshold value s; if dX or dY is greater than s, judging that the plane is suddenly changed; and if dz is larger than the jump threshold s, judging that the elevation is suddenly changed.
And detecting the track mutation region in the combined navigation track according to the mode.
S2, matching corresponding point cloud segments based on GPStime fields of the track mutation areas, wherein the corresponding point cloud segments are distorted point cloud segments.
It can be understood that, in the step S1, the track mutation area in the integrated navigation track is detected, and the corresponding LAZ point clouds are matched according to the GPStime field of the screened track mutation area, and these LAZ point clouds are distorted point clouds.
And S3, performing track point matching on the original combined navigation track of the track mutation region and the distorted point cloud segment to obtain an initial distorted point cloud track.
It can be understood that, in step S2, a distorted point cloud data segment in the point cloud data is found, and a point cloud track of the distorted point cloud segment needs to be obtained according to the track points of the track abrupt change region.
Specifically, performing track point matching on the original combined navigation track of the track mutation region and the distorted point cloud segment to obtain an initial distorted point cloud track, including: acquiring the minimum timestamp minTime and the maximum timestamp maxTime in all the distorted point cloud segments, forming a time period (minTime, maxTime) and acquiring the start and stop timestamps of each frame of point cloud according to the frame number field of the point cloud; extracting an effective integrated navigation track of a time period (minTime, maxTime) from the original integrated navigation track according to the time period; and matching track points by adopting a time nearest neighbor matching method according to the starting time of each frame of point cloud in the distorted point cloud segment, and matching each frame of point cloud with a time nearest neighbor track point to obtain matched point cloud track points, wherein the point cloud track points form an initial distorted point cloud track L0.
For the Roll, pitch, heading, vEast, vNorth, vUp field of the distorted point cloud trajectory, roll, pitch, heading three attitude angles and northeast speed (vEast, vNorth, vUp) are converted to an upper right front coordinate system of the vehicle body system, with the x coordinate axis on the right, the y coordinate axis on the front, and the z coordinate axis on the top.
According to the speed of the y axis of the vehicle system, the speed is less than or equal to 0, the moment can be judged to be parking or reversing, and the point cloud frames are marked to be parking or reversing point cloud.
And S4, updating the initial distorted point cloud track based on the speed, the time interval and the inter-frame distance increment of each frame point cloud track point of the initial distorted point cloud track, and recalculating the point cloud to obtain the distorted point cloud track after the first updating.
It can be understood that, for the initial distorted point cloud track, updating and optimizing the initial distorted point cloud track is required, specifically, according to the northeast day speed of each frame of point cloud track point of the initial distorted point cloud track, the time interval of each two adjacent frames of point clouds and the inter-frame distance increment of the northeast day of each two adjacent frames of point clouds, the track point of the initial frame is taken as a starting point, the inter-frame distance increment of each two adjacent frames of point clouds is integrated, and the initial distorted point cloud track is updated.
Assuming that the starting point is a, the distance between two adjacent frames is Di (i=1, 2..and n-1), the track point of the 2 nd frame is a+d1, the track point of the 3 rd frame is a+d1+d2, and the track point of the n-th frame is a+d1+d2+dn-1 in sequence; finally, a new track is obtained, and the new track replaces the old track.
And (3) based on the updated initial twisted point cloud track, re-calculating the point cloud, subtracting the original track point coordinates in the initial twisted point cloud track from the point cloud coordinates of each frame, and adding the track point coordinates in the updated initial twisted point cloud track to obtain the twisted point cloud track after the first updating.
And S5, extracting characteristic information of each frame of point cloud track points based on the first updated distorted point cloud track, matching based on the characteristic information of two adjacent frames of point cloud track points, obtaining inter-frame matching optimization parameters, updating the first updated distorted point cloud track based on the inter-frame matching optimization parameters, and obtaining the second updated distorted point cloud track.
It can be understood that, referring to fig. 2, for the first time of updating the optimized twisted point cloud track, features of each frame of point cloud track points are extracted, specifically, fast corner detection is adopted to extract corner points, and intensity information is used to binarize and extract lane lines of a single frame of point cloud; matching corner points and lane lines with the same name for front and rear adjacent frame point clouds to obtain inter-frame matching optimization parameters, namely an inter-frame distance increment correction value; and correcting the original inter-frame distance increment value according to the inter-frame distance increment correction value, and further updating the first updated distorted point cloud track to obtain a second updated distorted point cloud track.
And S6, calculating an error distribution value based on the twisted point cloud track updated for the second time, and optimizing the twisted point cloud track updated for the second time according to the error distribution value to obtain a final optimized point cloud track.
It can be understood that, for the twisted point cloud track updated for the second time, optimization is performed again, specifically, track tail point coordinates of the twisted point cloud track updated for the second time are obtained, original track points in the original twisted point cloud track are matched through time stamps of the track tail point coordinates, and position difference values (dX 1, dY1 and dZ 1) of the two points are calculated; setting an error distribution threshold c, such as 0.01, and calculating the maximum track point number N=max (i dX1/c i, |dY1/c|, |dZ1/c|) meeting the error distribution threshold according to the position difference values (dX 1, dY1 and dZ 1), namely, the track point number with the position difference value smaller than the error distribution threshold; and calculating an error distribution value D= (dX 1/N, dY1/N and dZ 1/N), and distributing errors to the first updated distorted point cloud track by taking the edge points as error distribution starting points to obtain the final optimized point cloud track.
For example, the border point is a starting point A1, N-1 track points are selected backward according to the track time, and the first track point a1=a1+d is allocated in error; second trace point a2=a2+ (N-1)/n×d; the i-th trace point ai=ai+ (N-i+1)/n×d, thereby updating and optimizing each trace point.
Finally, constructing a track point model based on a 3-time b spline function according to the updated and optimized point cloud track; and converting the point cloud frame set to a utm coordinate system again based on the finally optimized point cloud track and the point cloud frame set under the vehicle body system to obtain corrected and repaired point cloud data.
Referring to fig. 3, the overall flowchart of the point cloud data restoration method provided by the invention mainly comprises the following steps:
1. reading the combined navigation track and the point cloud data of the mobile measurement system, detecting a track mutation area from the combined navigation track, and acquiring an effective track of the track mutation area;
2. finding a twisted point cloud segment corresponding to the track abrupt change region in the point cloud data, and acquiring a corresponding initial twisted point cloud track;
3. and updating and optimizing the initial distorted point cloud track by adopting different methods to obtain final optimized point cloud data.
4. And constructing a track model based on the finally optimized point cloud data and performing coordinate transformation to obtain corrected and repaired point cloud data.
Fig. 4 is a block diagram of a point cloud data repair system according to an embodiment of the present invention, including 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:
the detection module 401 is configured to detect a track mutation region in an obtained integrated navigation track based on the integrated navigation track of the mobile measurement system;
a matching module 402, configured to match a corresponding point cloud segment based on a GPStime field of the track mutation region, where the corresponding point cloud segment is a distorted point cloud segment; performing track point matching on the original combined navigation track of the track mutation region and the distorted point cloud segment to obtain an initial distorted point cloud track;
a first updating module 403, configured to update the initial distorted point cloud track based on the speed, the time interval, and the inter-frame distance increment of each frame of the point cloud track point of the initial distorted point cloud track, and re-calculate a point cloud, to obtain a distorted point cloud track after the first update;
the second updating module 404 is configured to extract feature information of each frame of point cloud track points based on the first updated twisted point cloud track, match the feature information of two adjacent frames of point cloud track points to obtain an inter-frame matching optimization parameter, update the first updated twisted point cloud track based on the inter-frame matching optimization parameter, and obtain a second updated twisted point cloud track;
and the third updating module 405 is configured to calculate an error distribution value based on the second updated twisted point cloud track, and optimize the second updated twisted point cloud track according to the error distribution value, so as to obtain a final optimized point cloud track.
It can be understood that the point cloud data restoration system provided by the present invention corresponds to the point cloud data restoration method provided by the foregoing embodiments, and relevant technical features of the point cloud data restoration system may refer to relevant technical features of the point cloud data restoration method, which are not described herein.
Referring to fig. 5, fig. 5 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 5, an embodiment of the present invention provides an electronic device, including a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and capable of running on the processor 520, where the processor 520 executes the computer program 511 to implement the steps of the point cloud data restoration method.
Referring to fig. 6, fig. 6 is a schematic diagram of an embodiment of a computer readable storage medium according to the present invention. As shown in fig. 6, the present embodiment provides a computer-readable storage medium 600 on which a computer program 611 is stored, which computer program 611 implements the steps of the point cloud data restoration method when executed by a processor.
According to the point cloud data restoration method and system, the point cloud data with accurate positions and smooth point cloud data can be obtained by restoring the jumping or twisting point cloud data, the problem that point cloud twisting and jumping occur is solved, and the point cloud data does not need to be collected again.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The point cloud data restoration method is characterized by comprising the following steps of:
acquiring an integrated navigation track of a mobile measurement system and detecting a track mutation area in the integrated navigation track;
matching corresponding point cloud segments based on GPStime timestamp fields of the track mutation areas, wherein the corresponding point cloud segments are distorted point cloud segments;
performing track point matching on the original combined navigation track of the track mutation region and the twisted point cloud segment to obtain an initial twisted point cloud track;
updating the initial distorted point cloud track based on the speed, the time interval and the inter-frame distance increment of each frame point cloud track point of the initial distorted point cloud track, and recalculating the point cloud to obtain a distorted point cloud track after the first updating;
extracting characteristic information of each frame of point cloud track points based on the first updated distorted point cloud track, matching based on the characteristic information of two adjacent frames of point cloud track points, obtaining inter-frame matching optimization parameters, updating the first updated distorted point cloud track based on the inter-frame matching optimization parameters, and obtaining a second updated distorted point cloud track;
and calculating an error distribution value based on the twisted point cloud track updated for the second time, and optimizing the twisted point cloud track updated for the second time according to the error distribution value to obtain a final optimized point cloud track.
2. The method of point cloud data restoration according to claim 1, wherein the detecting a track mutation region in the integrated navigation track includes:
converting longitude and latitude coordinates of each track point in the integrated navigation track into utm coordinates;
extracting track points from each interval d of the combined navigation track based on a set track thinning threshold d to obtain a navigation track after thinning;
calculating coordinate differences (dX, dY, dZ) between two adjacent track points based on the navigation track after the thinning treatment, wherein dX, dY and dZ respectively represent a transverse coordinate difference value, a longitudinal coordinate difference value and an elevation coordinate difference value between the two track points;
setting a track mutation threshold value s, and judging the relation between the coordinate difference value between the two adjacent points of the track and the track mutation threshold value s; if dX or dY is greater than s, judging that the plane is suddenly changed; and if dz is larger than the jump threshold s, judging that the elevation is suddenly changed.
3. The method for repairing point cloud data according to claim 1, wherein performing track point matching on the original combined navigation track of the track abrupt change region and the distorted point cloud segment to obtain an initial distorted point cloud track, includes:
acquiring the minimum timestamp minTime and the maximum timestamp maxTime in all the distorted point cloud segments, forming a time period (minTime, maxTime) and acquiring the start and stop timestamps of each frame of point cloud according to the frame number field of the point cloud;
extracting an effective integrated navigation track of a time period (minTime, maxTime) from the original integrated navigation track according to the time period;
and matching track points by adopting a time nearest neighbor matching method according to the starting time of each frame of point cloud in the distorted point cloud segment, and matching each frame of point cloud with a time nearest neighbor track point to obtain an initial distorted point cloud track L0.
4. A point cloud data restoration method according to claim 1 or 3, wherein updating the initial distorted point cloud trajectory based on the speed, the time interval, and the inter-frame distance increment of each frame point cloud trajectory point of the initial distorted point cloud trajectory, and re-resolving the point cloud, to obtain a distorted point cloud trajectory after the first updating, includes:
according to the northeast day speed of each frame of point cloud track point of the initial distorted point cloud track, the time interval of every two adjacent frames of point clouds and the inter-frame distance increment of every two adjacent frames of point clouds, integrating the inter-frame distance increment of every two adjacent frames of point clouds by taking the track point of the initial frame as a starting point, and updating the initial distorted point cloud track;
and (3) based on the updated initial twisted point cloud track, re-calculating the point cloud, subtracting the original track point coordinates in the initial twisted point cloud track from the point cloud coordinates of each frame, and adding the track point coordinates in the updated initial twisted point cloud track to obtain the twisted point cloud track after the first updating.
5. The method for repairing point cloud data according to claim 4, wherein extracting feature information of each frame of point cloud track points based on the first updated distorted point cloud track, matching based on feature information of two adjacent frames of point cloud track points, obtaining an inter-frame matching optimization parameter, updating the first updated distorted point cloud track based on the inter-frame matching optimization parameter, and obtaining a second updated distorted point cloud track, comprises:
based on each frame of point cloud in the first updated distorted point cloud track, adopting fast corner detection to extract corner points, and using intensity information binarization to extract lane lines of single frame of point cloud;
matching corner points and lane lines with the same name for front and rear adjacent frame point clouds to obtain inter-frame matching optimization parameters, namely an inter-frame distance increment correction value;
and updating the first updated distorted point cloud track according to the inter-frame distance increment correction value to obtain a second updated distorted point cloud track.
6. The method for repairing point cloud data according to claim 1, wherein calculating an error distribution value based on the second updated distorted point cloud trajectory, and optimizing the second updated distorted point cloud trajectory according to the error distribution value to obtain a final optimized point cloud trajectory comprises:
acquiring track tail point coordinates of the second updated distorted point cloud track, matching original track points in the original distorted point cloud track through a timestamp of the track tail point coordinates, and calculating position difference values (dX 1, dY1 and dZ 1) of the two points;
setting an error distribution threshold c, and calculating a maximum track point number N=max (i dX1/c I, |dY1/c I, |dZ1/c I) meeting the error distribution threshold according to the position difference values (dX 1, dY1 and dZ 1);
and calculating error distribution values (dX 1/N, dY1/N and dZ 1/N), and distributing errors to the first updated distorted point cloud track by taking the edge points as error distribution starting points to obtain the final optimized point cloud track.
7. The method for repairing point cloud data according to any one of claims 1 to 6, wherein the obtaining the final optimized point cloud trajectory further comprises:
constructing a track point model based on a 3-time b spline function according to the finally optimized point cloud track;
and converting the point cloud track to a utm coordinate system again based on the finally optimized point cloud track and the point cloud frame set under the vehicle body system to obtain corrected point cloud data.
8. A point cloud data repair system, comprising:
the detection module is used for detecting a track mutation area in the integrated navigation track based on the acquired integrated navigation track of the mobile measurement system;
the matching module is used for matching corresponding point cloud segments based on the GPStime field of the track mutation area, wherein the corresponding point cloud segments are distorted point cloud segments; performing track point matching on the original combined navigation track of the track mutation region and the distorted point cloud segment to obtain an initial distorted point cloud track;
the first updating module is used for updating the initial distorted point cloud track based on the speed, the time interval and the inter-frame distance increment of each frame point cloud track point of the initial distorted point cloud track, and re-calculating the point cloud to obtain the distorted point cloud track after the first updating;
the second updating module is used for extracting characteristic information of each frame of point cloud track points based on the first updated distorted point cloud track, matching the characteristic information of every two adjacent frames of point cloud track points to obtain inter-frame matching optimization parameters, updating the first updated distorted point cloud track based on the inter-frame matching optimization parameters, and obtaining a second updated distorted point cloud track;
and the third updating module is used for calculating an error distribution value based on the twisted point cloud track after the second updating, optimizing the twisted point cloud track after the second updating according to the error distribution value, and obtaining the finally optimized point cloud track.
9. An electronic device comprising a memory, a processor for implementing the steps of the point cloud data repair method according to any one of claims 1-7 when executing a computer management class program stored in the memory.
10. A computer readable storage medium, having stored thereon a computer management class program which when executed by a processor implements the steps of the point cloud data restoration method according to any of claims 1-7.
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