CN111462072A - Dot cloud picture quality detection method and device and electronic equipment - Google Patents
Dot cloud picture quality detection method and device and electronic equipment Download PDFInfo
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Abstract
The application discloses a method and a device for detecting quality of a point cloud picture and electronic equipment, and relates to the technical field of automatic driving. The specific implementation scheme is as follows: dividing each frame of point cloud in a three-dimensional point cloud picture to be subjected to quality detection according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area; aiming at each area, acquiring the marker posture of each sub map in the area; determining offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area; according to the offset information between any two sub-maps in each area, the quality detection result of the three-dimensional point cloud picture is determined, so that the double image problem can be detected, and when the three-dimensional point cloud picture has a problem, the position of the problem can be positioned, and the quality detection efficiency is improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to the technical field of automatic driving, and particularly relates to a method and a device for detecting quality of a point cloud picture and electronic equipment.
Background
At present, during the automatic driving process of an unmanned vehicle, laser is periodically emitted to the periphery, and a frame of laser point cloud is generated according to the reflection value of the laser. And optimizing and splicing the laser point clouds of all frames to obtain a three-dimensional point cloud picture, and positioning the unmanned vehicle based on the three-dimensional point cloud picture.
At present, a quality detection method of a three-dimensional point cloud picture mainly comprises the steps of mapping the three-dimensional point cloud picture onto a two-dimensional positioning map, determining the accurate pose of each frame of laser point cloud according to the initial pose and the positioning map of each frame of laser point cloud, determining an error amount according to the accurate pose and the optimized pose of each optimized laser point cloud, determining whether the point cloud picture has a quality problem according to the error amount of each frame of laser point cloud, and determining whether the point cloud picture has a double image problem according to manual work.
In the above scheme, the ghost problem is difficult to detect, and the ghost problem needs to be detected manually. In addition, although it is possible to detect a problem in the three-dimensional point cloud image, it is difficult to determine which frame of laser point cloud has a problem, and therefore, the quality detection efficiency is poor.
Disclosure of Invention
The application provides a point cloud picture quality detection method, a point cloud picture quality detection device and electronic equipment, wherein each frame of point cloud in a three-dimensional point cloud picture to be subjected to quality detection is divided according to regions and acquisition time stamps to obtain a plurality of regions and a plurality of sub-maps in each region; aiming at each area, acquiring the marker posture of each sub map in the area; determining offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area; according to the offset information between any two sub-maps in each area, the quality detection result of the three-dimensional point cloud picture is determined, so that the double image problem can be detected, and when the three-dimensional point cloud picture has a problem, the position of the problem can be positioned, and the quality detection efficiency is improved.
An embodiment of a first aspect of the present application provides a method for detecting quality of a point cloud picture, including: dividing each frame of point cloud in a three-dimensional point cloud picture to be subjected to quality detection according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area;
for each area, acquiring the marker posture of each sub-map in the area;
determining offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area;
and determining the quality detection result of the three-dimensional point cloud picture according to the offset information between any two sub-maps in each area.
In one embodiment of the present application, the marker poses of the sub-map include: attitude information of a plurality of markers in the sub-map;
the pose information of the marker includes: x-axis coordinate information, Y-axis coordinate information, and Z-axis coordinate information.
In an embodiment of the present application, the obtaining, for each area, a marker pose of each sub-map in the area includes:
aiming at each sub-map of each area, mapping the sub-map into a two-dimensional coordinate system to obtain a two-dimensional sub-map;
determining X-axis coordinate information and Y-axis coordinate information of each marker in the sub-map by combining a two-dimensional sub-map and an Euclidean clustering algorithm;
and determining the Z-axis coordinate information of each marker in the sub-map by combining the sub-map and the X-axis coordinate information and the Y-axis coordinate information of each marker in the sub-map.
In an embodiment of the present application, the determining offset information between any two sub-maps in the area according to the marker pose of each sub-map in the area includes:
aiming at any two sub-maps in the area, carrying out pairing operation on the attitude information of a plurality of markers in the two sub-maps to obtain a plurality of marker pairs;
determining the offset information of the marker pair according to the attitude information of the markers in each marker pair;
determining offset information between the two sub-maps from the offset information of each of the plurality of marker pairs.
In an embodiment of the present application, the pairing, for any two sub-maps in the area, gesture information of multiple markers in the two sub-maps to obtain multiple marker pairs includes:
aiming at any two sub-maps in the area, acquiring a first sub-map and a second sub-map in the two sub-maps;
for each marker to be paired in the first sub-map, obtaining distance information between the attitude information of the marker to be paired and the attitude information of each marker in the second sub-map;
determining a paired marker corresponding to the marker to be paired in the second sub-map according to the distance information;
and generating a marker pair according to the marker to be paired and the corresponding paired marker.
In an embodiment of the application, the determining offset information between the two sub-maps according to the offset information of each of the plurality of marker pairs includes:
sorting the X-axis offset information of the plurality of marker pairs, and determining a median value in a sorting result as the X-axis offset information between the two sub-maps;
sorting the Y-axis offset information of the plurality of marker pairs, and determining a median value in a sorting result as Y-axis offset information between the two sub-maps;
fusing the two sub-maps according to the X-axis offset information and the Y-axis offset information between the two sub-maps, and performing grid division on the fused sub-maps to obtain a plurality of grids;
aiming at each grid, obtaining a median value of Z-axis coordinate information of markers respectively belonging to two sub-maps in the grid, and determining a difference value of the two median values as Z-axis offset information of the grid;
and determining Z-axis offset information between the two sub-maps according to the Z-axis offset information of the grids.
In an embodiment of the present application, after determining offset information between any two sub-maps in the area according to the marker pose of each sub-map in the area, the method further includes:
optimizing the offset information between any two sub-maps in the area according to the offset information between any two sub-maps in the area and a preset offset constraint condition to obtain the optimized offset information between any two sub-maps in the area;
correspondingly, the determining the quality detection result of the three-dimensional point cloud picture according to the offset information between any two sub-maps in each area includes:
and determining the quality detection result of the three-dimensional point cloud picture according to the optimized offset information between any two sub-maps in each area.
In an embodiment of the present application, the determining a quality detection result of the three-dimensional point cloud graph according to offset information between any two sub-maps in each area includes:
judging whether offset information between any two sub-maps in each area meets a preset offset condition or not;
and determining the two sub-maps with the corresponding offset information meeting the offset condition as the sub-maps with quality problems.
In one embodiment of the application, in the multiple regions of the three-dimensional point cloud image, an overlapped part exists between any two adjacent regions.
According to the point cloud picture quality detection method, each frame of point cloud in a three-dimensional point cloud picture to be subjected to quality detection is divided according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area; aiming at each area, acquiring the marker posture of each sub map in the area; determining offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area; according to the offset information between any two sub-maps in each area, the quality detection result of the three-dimensional point cloud picture is determined, so that the double image problem can be detected, and when the three-dimensional point cloud picture has a problem, the position of the problem can be positioned, and the quality detection efficiency is improved.
An embodiment of a second aspect of the present application provides a device for detecting quality of a point cloud picture, including:
the dividing module is used for dividing each frame of point cloud in the three-dimensional point cloud picture to be subjected to quality detection according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area;
the acquisition module is used for acquiring the marker gesture of each sub-map in each area;
the first determination module is used for determining the offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area;
and the second determining module is used for determining the quality detection result of the three-dimensional point cloud picture according to the offset information between any two sub-maps in each area.
In one embodiment of the present application, the marker poses of the sub-map include: attitude information of a plurality of markers in the sub-map;
the pose information of the marker includes: x-axis coordinate information, Y-axis coordinate information, and Z-axis coordinate information.
In an embodiment of the present application, the obtaining module is specifically configured to map, for each sub-map of each area, the sub-map into a two-dimensional coordinate system to obtain a two-dimensional sub-map;
determining X-axis coordinate information and Y-axis coordinate information of each marker in the sub-map by combining a two-dimensional sub-map and an Euclidean clustering algorithm;
and determining the Z-axis coordinate information of each marker in the sub-map by combining the sub-map and the X-axis coordinate information and the Y-axis coordinate information of each marker in the sub-map.
In an embodiment of the application, the first determining module is specifically configured to, for any two sub-maps in the area, perform pairing operation on posture information of multiple markers in the two sub-maps to obtain multiple marker pairs;
determining the offset information of the marker pair according to the attitude information of the markers in each marker pair;
determining offset information between the two sub-maps from the offset information of each of the plurality of marker pairs.
In an embodiment of the application, the first determining module is specifically configured to, for any two sub-maps in the area, obtain a first sub-map and a second sub-map of the two sub-maps;
for each marker to be paired in the first sub-map, obtaining distance information between the attitude information of the marker to be paired and the attitude information of each marker in the second sub-map;
determining a paired marker corresponding to the marker to be paired in the second sub-map according to the distance information;
and generating a marker pair according to the marker to be paired and the corresponding paired marker.
In an embodiment of the application, the first determining module is specifically configured to sort the X-axis offset information of the plurality of marker pairs, and determine a median value in a sorting result as the X-axis offset information between the two sub-maps;
sorting the Y-axis offset information of the plurality of marker pairs, and determining a median value in a sorting result as Y-axis offset information between the two sub-maps;
fusing the two sub-maps according to the X-axis offset information and the Y-axis offset information between the two sub-maps, and performing grid division on the fused sub-maps to obtain a plurality of grids;
aiming at each grid, obtaining a median value of Z-axis coordinate information of markers respectively belonging to two sub-maps in the grid, and determining a difference value of the two median values as Z-axis offset information of the grid;
and determining Z-axis offset information between the two sub-maps according to the Z-axis offset information of the grids.
In one embodiment of the present application, the apparatus further comprises: the optimization module is used for optimizing the offset information between any two sub-maps in the area according to the offset information between any two sub-maps in the area and a preset offset constraint condition to obtain optimized offset information between any two sub-maps in the area;
correspondingly, the second determining module is specifically configured to determine a quality detection result of the three-dimensional point cloud picture according to the optimized offset information between any two sub-maps in each area.
In an embodiment of the application, the second determining module is specifically configured to, for each area, determine whether offset information between any two sub-maps in the area meets a preset offset condition;
and determining the two sub-maps with the corresponding offset information meeting the offset condition as the sub-maps with quality problems.
In one embodiment of the application, in the multiple regions of the three-dimensional point cloud image, an overlapped part exists between any two adjacent regions.
According to the point cloud picture quality detection device, each frame of point cloud in a three-dimensional point cloud picture to be subjected to quality detection is divided according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area; aiming at each area, acquiring the marker posture of each sub map in the area; determining offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area; according to the offset information between any two sub-maps in each area, the quality detection result of the three-dimensional point cloud picture is determined, so that the double image problem can be detected, and when the three-dimensional point cloud picture has a problem, the position of the problem can be positioned, and the quality detection efficiency is improved.
An embodiment of a third aspect of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of point cloud quality detection as described above.
A fourth aspect of the present application is directed to a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for detecting the quality of a point cloud image as described above.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present application;
FIG. 2 is a schematic illustration of a plurality of regions in a three-dimensional point cloud;
FIG. 3 is a schematic view of a shaft in a sub-map;
FIG. 4 is a schematic illustration according to a second embodiment of the present application;
FIG. 5 is a schematic diagram of two sub-maps with a horizontal offset between them;
FIG. 6 is a schematic diagram of two sub-maps with a vertical offset between them;
FIG. 7 is a schematic illustration according to a third embodiment of the present application;
FIG. 8 is a schematic illustration according to a fourth embodiment of the present application;
fig. 9 is a block diagram of an electronic device for implementing a method for detecting quality of a point cloud image according to an embodiment of the present disclosure.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The following describes a method, an apparatus, and an electronic device for detecting quality of a cloud point image according to an embodiment of the present application with reference to the drawings.
Fig. 1 is a schematic diagram according to a first embodiment of the present application. It should be noted that the main execution body of the method for detecting the quality of the point cloud image provided in this embodiment is a point cloud image quality detection apparatus, and the point cloud image quality detection apparatus may specifically be a hardware device, or software in the hardware device, or the like. The hardware devices are, for example, terminal devices, servers, and the like.
As shown in fig. 1, the specific implementation process of the point cloud picture quality detection method is as follows:
In this embodiment, the three-dimensional point cloud image to be subjected to quality detection is obtained in such a manner that, during the running process of each vehicle, at each acquisition time point, the vehicle emits a laser signal to the surrounding environment, a frame of laser point cloud is generated according to the laser signal reflected by the surrounding environment, and each laser point cloud corresponds to one offset information of the vehicle relative to the initial position; and splicing the laser point clouds according to the corresponding offset information to generate a three-dimensional point cloud picture to be subjected to quality detection.
In this embodiment, the manner in which the point cloud image quality detection device divides each frame of point cloud in the three-dimensional point cloud image to be subjected to quality detection according to the region and the acquisition timestamp may be that the three-dimensional point cloud image is divided according to the region first to obtain a plurality of regions. And aiming at each area, dividing according to the acquisition time stamp to obtain a plurality of sub-maps in the area. Specifically, for each region, frame point clouds with continuous acquisition timestamps can be spliced together, and frame point clouds with discontinuous acquisition timestamps cannot be spliced together, so that each sub-map is obtained. For example, a sub-map may be generated by stitching frames of point clouds acquired during a vehicle traveling over an area. There is no offset information between frames of point clouds collected during a vehicle's travel once in an area. For example, if a three-dimensional point cloud image is obtained by stitching frame point clouds acquired by 8 times of vehicle driving, each region in the three-dimensional point cloud image may include 8 sub-maps.
In order to ensure that the offset information between adjacent regions in the three-dimensional point cloud image can be detected and improve the quality detection effect of the three-dimensional point cloud image, a superposition part needs to exist between any two adjacent regions. A schematic of the regions in the three-dimensional point cloud may be as shown in fig. 2, for example.
And 102, acquiring the marker posture of each sub map in the area aiming at each area.
In this embodiment, the marker gestures of the sub-map include: attitude information of a plurality of markers in the sub-map; the pose information of the marker includes: x-axis coordinate information, Y-axis coordinate information, and Z-axis coordinate information. Correspondingly, the process of the point cloud image quality detection apparatus executing step 102 may be, for example, mapping the sub-map into a two-dimensional coordinate system for each sub-map of each area to obtain a two-dimensional sub-map; determining X-axis coordinate information and Y-axis coordinate information of each marker in the sub-map by combining the two-dimensional sub-map and an Euclidean clustering algorithm; and determining the Z-axis coordinate information of each marker in the sub-map by combining the sub-map and the X-axis coordinate information and the Y-axis coordinate information of each marker in the sub-map. In addition, the pose information of the marker may further include: x-axis direction angle information, Y-axis direction angle information, and Z-axis direction angle information.
And 103, determining offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area.
In this embodiment, the marker may be, for example, a shaft, a ground surface, or the like. Such as an antenna mast, a light pole, etc. In this embodiment, a schematic diagram of the shaft in the sub-map may be as shown in fig. 3, for example.
And 104, determining the quality detection result of the three-dimensional point cloud picture according to the offset information between any two sub-maps in each area.
In this embodiment, the process of the point cloud image quality detection apparatus executing step 104 may specifically be that, for each area, whether offset information between any two sub-maps in the area meets a preset offset condition is determined; and determining the two sub-maps with the corresponding offset information meeting the offset condition as the sub-maps with quality problems.
In this embodiment, the offset information between any two sub-maps includes: x-axis offset information, Y-axis offset information, and Z-axis offset information. In this embodiment, the offset condition may specifically be that the horizontal direction offset is smaller than the horizontal direction offset threshold, and the vertical direction offset is smaller than the vertical direction offset threshold. The horizontal direction offset is the sum of squares of the X-axis offset information and the Y-axis offset information. And the vertical direction offset is Z-axis offset information.
In this embodiment, if the offset information between any two sub-maps in each area satisfies the preset offset condition, the three-dimensional point cloud map has no quality problem. If two sub-maps exist and the offset information between the two sub-maps meets the offset condition, the two sub-maps have quality problems. The two sub-maps are the positions where the quality problem exists in the three-dimensional point cloud image.
In this embodiment, in order to ensure the reasonableness of the offset information between any two sub-maps in the area and ensure that the sum of the offset information between any two sub-maps is 0, after step 103, the method may further include: and optimizing the offset information between any two sub-maps in the area according to the offset information between any two sub-maps in the area and a preset offset constraint condition to obtain the optimized offset information between any two sub-maps in the area. Correspondingly, step 104 may specifically be to determine a quality detection result of the three-dimensional point cloud graph according to the optimized offset information between any two sub-maps in each area.
In this embodiment, the offset constraint condition is that the sum of offset information between any two sub-maps in the area is 0. For example, if an area includes three sub-maps A, B and C; the offset information between a and B is D1, the offset information between B and C is D2, and the offset information between C and a is D3, then D1+ D2+ D3 is 0. If the sum of the offset information between the three sub-maps is not 0, it is unreasonable that the offset information between the three sub-maps needs to be optimally adjusted so that the sum of the offset information between the three sub-maps is 0.
In this embodiment, after the quality detection result of the three-dimensional point cloud image is determined, the sub-map with the quality problem may be optimized according to the quality detection result.
According to the point cloud picture quality detection method, each frame of point cloud in a three-dimensional point cloud picture to be subjected to quality detection is divided according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area; aiming at each area, acquiring the marker posture of each sub map in the area; determining offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area; according to the offset information between any two sub-maps in each area, the quality detection result of the three-dimensional point cloud picture is determined, so that the double image problem can be detected, and when the three-dimensional point cloud picture has a problem, the position of the problem can be positioned, and the quality detection efficiency is improved.
Fig. 4 is a schematic diagram according to a second embodiment of the present application. As shown in fig. 4, step 103 may specifically include the following steps:
In this embodiment, the process of the point cloud image quality detection apparatus executing step 401 may specifically be that, for any two sub-maps in the area, a first sub-map and a second sub-map in the two sub-maps are obtained; aiming at each marker to be paired in the first sub map, acquiring distance information between the attitude information of the marker to be paired and the attitude information of each marker in the second sub map; determining a paired marker corresponding to the marker to be paired in the second sub-map according to the distance information; and generating a marker pair according to the to-be-paired markers and the corresponding paired markers.
In this embodiment, according to the distance information, the paired markers corresponding to the to-be-paired markers in the second sub-map may be determined in a manner that the markers corresponding to the distance information in the second sub-map, which is less than or equal to a preset distance threshold, are determined as the paired markers corresponding to the to-be-paired markers; and if the distance information between the to-be-paired marker and each marker in the second sub-map is greater than the preset distance threshold, determining that the paired marker corresponding to the to-be-paired marker does not exist in the second sub-map.
In this embodiment, in the offset information of the marker pair, the offset information of the X axis is a difference value of X axis coordinate information of two markers in the marker pair; the Y-axis offset information is the difference in Y-axis coordinate information of the two markers in the marker pair.
In this embodiment, the posture information of the marker includes: x-axis coordinate information, Y-axis coordinate information, and Z-axis coordinate information. The process of the cloud point image quality detection apparatus executing step 403 may specifically be that the X-axis offset information of a plurality of marker pairs is sorted, and a median value in the sorting result is determined as the X-axis offset information between two sub-maps; sorting the Y-axis offset information of the plurality of marker pairs, and determining a median value in a sorting result as Y-axis offset information between the two sub-maps; fusing the two sub-maps according to X-axis offset information and Y-axis offset information between the two sub-maps, and performing grid division on the fused sub-maps to obtain a plurality of grids; aiming at each grid, obtaining a median value of Z-axis coordinate information of the markers belonging to the two sub-maps in the grid respectively, and determining the difference value of the two median values as Z-axis offset information of the grid; and determining Z-axis offset information between the two sub-maps according to the Z-axis offset information of the grids.
In this embodiment, according to the X-axis offset information and the Y-axis offset information between the two sub-maps, the two sub-maps may be fused in a manner that, according to the X-axis offset information and the Y-axis offset information between the two sub-maps, the two sub-maps are subjected to position adjustment and splicing to obtain a fused sub-map. In the merged sub-map, the distances of the X-axis coordinate information of the two markers in each marker pair are small or consistent, and the distances of the Y-axis coordinate information of the two markers are small or consistent.
In this embodiment, the size of each grid may be, for example, 0.1m ﹡ 0.1.1 m. Assuming that the two sub-maps are Si and Sj, respectively, in the first grid, N markers belong to the sub-map Si and M markers belong to the sub-map Sj. Acquiring Z-axis coordinate information of N markers, and sorting and taking a median; acquiring Z-axis coordinate information of M markers, and sorting and taking a median; and determining the difference value of the two median values as Z-axis offset information of the grid.
In this embodiment, the cloud point image quality detection apparatus may determine the Z-axis offset information between the two sub-maps according to the Z-axis offset information of the multiple grids, by sorting the Z-axis offset information of the multiple grids, taking a median value, and determining the median value as the Z-axis offset information between the two sub-maps.
In this embodiment, it should be noted that the offset information is a signed value, for example, if the offset information of the first sub-map relative to the second sub-map is (DX, DY, DZ), the offset information of the second sub-map relative to the first sub-map is (-DX, -DY, -DZ). For some special cases, for example, where there is an angular rotation between the two sub-maps about the Z-axis, and the shaft distribution is relatively symmetric with respect to the X-axis and the Y-axis, the resulting DX, DY may be caused to be close to (0, 0). For example, if there is a rotation of a certain angle between the two sub-maps around the X-axis or the Y-axis, and the rotation axis is located at the center of the two sub-maps, the DZ may be close to 0. Therefore, when sorting the X-axis offset information, the X-axis offset information needs to be subjected to absolute value processing, and then sorting and median value taking are performed; when sorting the Y-axis offset information, it is necessary to perform absolute value processing on the Y-axis offset information, and then perform sorting and median selection.
In this embodiment, a schematic diagram when a horizontal direction offset exists between two sub-maps may be as shown in fig. 5, and a schematic diagram when a vertical direction offset exists between two sub-maps may be as shown in fig. 6.
According to the point cloud picture quality detection method, each frame of point cloud in a three-dimensional point cloud picture to be subjected to quality detection is divided according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area; aiming at each area, acquiring the marker posture of each sub map in the area; aiming at any two sub-maps in the area, carrying out pairing operation on the attitude information of a plurality of markers in the two sub-maps to obtain a plurality of marker pairs; determining the offset information of the marker pair according to the attitude information of the markers in each marker pair; determining offset information between the two sub-maps according to the offset information of each of the plurality of marker pairs; according to the offset information between any two sub-maps in each area, the quality detection result of the three-dimensional point cloud picture is determined, so that the double image problem can be detected, and when the three-dimensional point cloud picture has a problem, the position of the problem can be positioned, and the quality detection efficiency is improved.
In order to implement the embodiments described in fig. 1 to fig. 4, an embodiment of the present application further provides a device for detecting quality of a point cloud chart.
Fig. 7 is a schematic diagram according to a third embodiment of the present application. As shown in fig. 7, the apparatus 700 for detecting quality of a cloud image includes: a dividing module 710, an obtaining module 720, a first determining module 730, and a second determining module 740.
The dividing module 710 is configured to divide each frame of point cloud in a three-dimensional point cloud image to be quality-detected according to a region and an acquisition timestamp to obtain a plurality of regions and a plurality of sub-maps in each region;
an obtaining module 720, configured to obtain, for each area, a marker pose of each sub-map in the area;
a first determining module 730, configured to determine offset information between any two sub-maps in the area according to the marker pose of each sub-map in the area;
the second determining module 740 is configured to determine a quality detection result of the three-dimensional point cloud graph according to offset information between any two sub-maps in each area.
In one embodiment of the present application, the marker poses of the sub-map include: attitude information of a plurality of markers in the sub-map;
the pose information of the marker includes: x-axis coordinate information, Y-axis coordinate information, and Z-axis coordinate information.
In an embodiment of the present application, the obtaining module 720 is specifically configured to, for each sub-map of each area, map the sub-map into a two-dimensional coordinate system to obtain a two-dimensional sub-map;
determining X-axis coordinate information and Y-axis coordinate information of each marker in the sub-map by combining a two-dimensional sub-map and an Euclidean clustering algorithm;
and determining the Z-axis coordinate information of each marker in the sub-map by combining the sub-map and the X-axis coordinate information and the Y-axis coordinate information of each marker in the sub-map.
In an embodiment of the present application, the first determining module 730 is specifically configured to, for any two sub-maps in the area, perform pairing operation on the posture information of multiple markers in the two sub-maps to obtain multiple marker pairs;
determining the offset information of the marker pair according to the attitude information of the markers in each marker pair;
determining offset information between the two sub-maps from the offset information of each of the plurality of marker pairs.
In an embodiment of the present application, the first determining module 730 is specifically configured to, for any two sub-maps in the area, obtain a first sub-map and a second sub-map of the two sub-maps;
for each marker to be paired in the first sub-map, obtaining distance information between the attitude information of the marker to be paired and the attitude information of each marker in the second sub-map;
determining a paired marker corresponding to the marker to be paired in the second sub-map according to the distance information;
and generating a marker pair according to the marker to be paired and the corresponding paired marker.
In an embodiment of the present application, the first determining module 730 is specifically configured to sort the X-axis offset information of the plurality of marker pairs, and determine a median value in the sorting result as the X-axis offset information between the two sub-maps;
sorting the Y-axis offset information of the plurality of marker pairs, and determining a median value in a sorting result as Y-axis offset information between the two sub-maps;
fusing the two sub-maps according to the X-axis offset information and the Y-axis offset information between the two sub-maps, and performing grid division on the fused sub-maps to obtain a plurality of grids;
aiming at each grid, obtaining a median value of Z-axis coordinate information of markers respectively belonging to two sub-maps in the grid, and determining a difference value of the two median values as Z-axis offset information of the grid;
and determining Z-axis offset information between the two sub-maps according to the Z-axis offset information of the grids.
In an embodiment of the present application, with reference to fig. 8, on the basis of the embodiment shown in fig. 7, the apparatus further includes: the optimization module 750 is configured to optimize offset information between any two sub-maps in the area according to the offset information between any two sub-maps in the area and a preset offset constraint condition, so as to obtain optimized offset information between any two sub-maps in the area;
correspondingly, the second determining module 740 is specifically configured to determine the quality detection result of the three-dimensional point cloud graph according to the optimized offset information between any two sub-maps in each area.
In an embodiment of the present application, the second determining module 740 is specifically configured to, for each area, determine whether offset information between any two sub-maps in the area meets a preset offset condition;
and determining the two sub-maps with the corresponding offset information meeting the offset condition as the sub-maps with quality problems.
In one embodiment of the application, in the multiple regions of the three-dimensional point cloud image, an overlapped part exists between any two adjacent regions.
According to the point cloud picture quality detection device, each frame of point cloud in a three-dimensional point cloud picture to be subjected to quality detection is divided according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area; aiming at each area, acquiring the marker posture of each sub map in the area; determining offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area; according to the offset information between any two sub-maps in each area, the quality detection result of the three-dimensional point cloud picture is determined, so that the double image problem can be detected, and when the three-dimensional point cloud picture has a problem, the position of the problem can be positioned, and the quality detection efficiency is improved.
In order to implement the above embodiments, an electronic device is further provided in the embodiments of the present application.
Fig. 9 is a block diagram of an electronic device according to the method for detecting quality of a point cloud image in the embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 9, the electronic apparatus includes: one or more processors 501, memory 502, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 9 illustrates an example of one processor 501.
The memory 502, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method for detecting quality of a cloud point cloud (e.g., the partitioning module 710, the obtaining module 720, the first determining module 730, the second determining module 740 shown in fig. 7; and the optimizing module 750 shown in fig. 8) in the embodiments of the present application. The processor 501 executes various functional applications and data processing of the server by running non-transitory software programs, instructions and modules stored in the memory 502, so as to implement the method for detecting the quality of the cloud point map in the above method embodiments.
The memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the electronic device by the dot cloud quality inspection, and the like. Further, the memory 502 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 502 may optionally include memory located remotely from processor 501, which may be connected to cloud point quality detection electronics over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the point cloud picture quality detection method may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 9 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device for dot cloud quality detection, such as a touch screen, keypad, mouse, track pad, touch pad, pointing stick, one or more mouse buttons, track ball, joystick, etc. the output device 504 may include a display device, auxiliary lighting (e.g., L ED), and tactile feedback (e.g., vibration motor), etc.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable logic devices (P L D)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal.
The systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or L CD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer for providing interaction with the user.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., AN application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with AN implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (12)
1. A method for detecting the quality of a point cloud picture is characterized by comprising the following steps:
dividing each frame of point cloud in a three-dimensional point cloud picture to be subjected to quality detection according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area;
for each area, acquiring the marker posture of each sub-map in the area;
determining offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area;
and determining the quality detection result of the three-dimensional point cloud picture according to the offset information between any two sub-maps in each area.
2. The method of claim 1, wherein the marker poses of the sub-map comprise: attitude information of a plurality of markers in the sub-map;
the pose information of the marker includes: x-axis coordinate information, Y-axis coordinate information, and Z-axis coordinate information.
3. The method of claim 2, wherein the obtaining, for each region, a marker pose for each sub-map in the region comprises:
aiming at each sub-map of each area, mapping the sub-map into a two-dimensional coordinate system to obtain a two-dimensional sub-map;
determining X-axis coordinate information and Y-axis coordinate information of each marker in the sub-map by combining a two-dimensional sub-map and an Euclidean clustering algorithm;
and determining the Z-axis coordinate information of each marker in the sub-map by combining the sub-map and the X-axis coordinate information and the Y-axis coordinate information of each marker in the sub-map.
4. The method of claim 2, wherein determining offset information between any two sub-maps in the area based on the marker pose of each sub-map in the area comprises:
aiming at any two sub-maps in the area, carrying out pairing operation on the attitude information of a plurality of markers in the two sub-maps to obtain a plurality of marker pairs;
determining the offset information of the marker pair according to the attitude information of the markers in each marker pair;
determining offset information between the two sub-maps from the offset information of each of the plurality of marker pairs.
5. The method according to claim 4, wherein the pairing operation of the posture information of the plurality of markers in any two sub-maps of the area to obtain a plurality of marker pairs comprises:
aiming at any two sub-maps in the area, acquiring a first sub-map and a second sub-map in the two sub-maps;
for each marker to be paired in the first sub-map, obtaining distance information between the attitude information of the marker to be paired and the attitude information of each marker in the second sub-map;
determining a paired marker corresponding to the marker to be paired in the second sub-map according to the distance information;
and generating a marker pair according to the marker to be paired and the corresponding paired marker.
6. The method of claim 4, wherein determining offset information between the two sub-maps from the offset information of each of the plurality of marker pairs comprises:
sorting the X-axis offset information of the plurality of marker pairs, and determining a median value in a sorting result as the X-axis offset information between the two sub-maps;
sorting the Y-axis offset information of the plurality of marker pairs, and determining a median value in a sorting result as Y-axis offset information between the two sub-maps;
fusing the two sub-maps according to the X-axis offset information and the Y-axis offset information between the two sub-maps, and performing grid division on the fused sub-maps to obtain a plurality of grids;
aiming at each grid, obtaining a median value of Z-axis coordinate information of markers respectively belonging to two sub-maps in the grid, and determining a difference value of the two median values as Z-axis offset information of the grid;
and determining Z-axis offset information between the two sub-maps according to the Z-axis offset information of the grids.
7. The method of claim 1, wherein after determining offset information between any two sub-maps in the area according to the marker pose of each sub-map in the area, further comprising:
optimizing the offset information between any two sub-maps in the area according to the offset information between any two sub-maps in the area and a preset offset constraint condition to obtain the optimized offset information between any two sub-maps in the area;
correspondingly, the determining the quality detection result of the three-dimensional point cloud picture according to the offset information between any two sub-maps in each area includes:
and determining the quality detection result of the three-dimensional point cloud picture according to the optimized offset information between any two sub-maps in each area.
8. The method of claim 1, wherein the determining the quality detection result of the three-dimensional point cloud picture according to the offset information between any two sub-maps in each area comprises:
judging whether offset information between any two sub-maps in each area meets a preset offset condition or not;
and determining the two sub-maps with the corresponding offset information meeting the offset condition as the sub-maps with quality problems.
9. The method of claim 1, wherein there is an overlap between any two adjacent regions of the plurality of regions of the three-dimensional point cloud.
10. A point cloud picture quality detection device is characterized by comprising:
the dividing module is used for dividing each frame of point cloud in the three-dimensional point cloud picture to be subjected to quality detection according to the area and the acquisition timestamp to obtain a plurality of areas and a plurality of sub-maps in each area;
the acquisition module is used for acquiring the marker gesture of each sub-map in each area;
the first determination module is used for determining the offset information between any two sub-maps in the area according to the marker posture of each sub-map in the area;
and the second determining module is used for determining the quality detection result of the three-dimensional point cloud picture according to the offset information between any two sub-maps in each area.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-9.
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