Point cloud mapping method and system for area array Geiger APD laser radar

Abstract

The present invention discloses a point cloud mapping method and system for an area array Geiger APD laser radar, belonging to the technical field of laser mapping. The method transforms the coordinate system of the Geiger APD laser point cloud in the depth direction and transforms it into a relative coordinate system for registration, thereby weakening the dominant influence of large depth values ​​on posture estimation; directly registers the current frame point cloud with a relative global map, thereby reducing the cumulative error in the registration process; then, the number of points in the global map is maintained within a preset range to reduce computing resource pressure and improve the accuracy of registration, thereby achieving a dense point cloud mapping result for the target scene; and the enhanced point cloud is projectively transformed, ultimately achieving dense laser imaging of long-distance targets, thereby solving the technical problem of sparse long-distance imaging in existing Geiger APD laser mapping algorithms.

Description

Technical Field

[0001] The present invention belongs to the field of laser mapping technology, and more specifically, relates to a point cloud mapping method and system for an area array Geiger APD laser radar. Background Art

[0002] Laser construction Figure 1 Generally speaking, it refers to obtaining three-dimensional point cloud information by scanning the surrounding environment through lidar, estimating its own posture by extracting its three-dimensional features and performing point cloud registration, and then realizing the construction of a three-dimensional map of the surrounding environment. It is a popular research direction and is widely used in many fields such as autonomous driving, robot navigation, three-dimensional reconstruction and three-dimensional target detection.

[0003] Geiger APD lidar boasts advantages such as high frame rate, long detection range, and high detection accuracy, and has important applications in both military and civilian fields. However, due to its susceptibility to noise, the signal-to-noise ratio of the detection data is low. Furthermore, due to inherent design issues, the resolution of the collected range image is low. To address this noise issue, the laser data must be denoised, which results in a sparser point cloud. Even if hundreds of frames of point clouds collected by the laser detector are superimposed, the resulting point cloud remains sparse and cannot support subsequent tasks such as target detection and tracking.

[0004] Laser mapping is performed by registering the point cloud obtained by scanning a Geiger APD lidar. This enhances spatial information with temporal information, enriching the scene's three-dimensional structure to meet downstream task requirements. However, due to the sparse point cloud of a Geiger APD lidar, simple point cloud registration can easily lead to incorrect registration results, resulting in mapping failure. This is due to the following reasons: First, the long detection range of a Geiger APD lidar makes the point cloud registration algorithm susceptible to failure. Second, the point cloud it scans is sparse, and the scene features are not rich, which also increases the difficulty of point cloud registration. Finally, as the laser detector moves continuously, the spatial resolution of the point cloud changes significantly, and the imbalance of features also hinders the point cloud registration process. Implementing an accurate and robust long-range Geiger APD laser mapping algorithm remains an urgent problem. Summary of the Invention

[0005] In response to the above defects or improvement needs of the prior art, the present invention provides a point cloud mapping method for an area array Geiger APD laser radar, the purpose of which is to weaken the dominant influence of large depth values ​​on posture estimation by transforming the coordinate system of the Geiger APD laser point cloud in the depth direction and transforming it into a relative coordinate system for alignment; directly aligning the current frame point cloud with the relative global map can reduce the cumulative error in the alignment process; then, the number of points in the global map is maintained within a preset range to reduce the pressure on computing resources and improve the accuracy of the alignment, thereby achieving a dense point cloud mapping result of the target scene; the enhanced point cloud is projected and transformed, and finally dense laser imaging of long-distance targets is achieved, thereby solving the technical problem of sparse long-distance imaging of the existing Geiger APD laser mapping algorithm.

[0006] To achieve the above objectives, according to one aspect of the present invention, a point cloud mapping method for an area array Geiger APD laser radar is provided, comprising:

[0007] S1: Perform depth transformation on the absolute laser coordinate system of the current frame point cloud in the laser echo signal to convert the current frame point cloud in the laser coordinate system into the current frame point cloud in the relative laser coordinate system. , thereby weakening the influence of large depth values;

[0008] S2: Use the point cloud of the current frame and the previous frame in the relative laser coordinate system to obtain the precise posture transformation from the current frame to the previous frame , i is the current frame number, It is a mark of precise posture;

[0009] S3: Take the relative laser coordinate system of the first frame point cloud as the world coordinate system, and use the first Frame point cloud builds a relative global map of the previous frame ; ;right Perform point cloud downsampling; use the precise pose transformation from the previous frame to the first frame The exact pose transformation from the current frame to the previous frame , get the rough pose transformation from the current frame to the first frame , is the rough pose identification; using the downsampled 、 and Calculate the exact pose transformation from the current frame to the first frame ;

[0010] S4: Using the current frame point cloud and the corresponding Build a relative global map of the current frame ,set up The point threshold in is within the preset range;

[0011] S5: The relative global pose of the current frame Restore to absolute global pose ; for The inverse matrix of and Obtain an enhanced point cloud in the absolute laser coordinate system of the current frame; and project the enhanced point cloud in the absolute laser coordinate system of the current frame to obtain a dense depth map of the target scene.

[0012] In one embodiment, the S1 includes:

[0013] S11: Obtaining a count value map of a current frame in the laser echo signal, and calculating an absolute depth map of the current frame using the count value map and the corresponding delay time;

[0014] S12: Back-project the absolute depth map of the current frame into the corresponding laser coordinate system to obtain the point cloud of the current frame in the absolute laser coordinate system;

[0015] S13: performing depth-wise transformation on the absolute laser coordinate system to convert the current frame point cloud in the absolute laser coordinate system into the current frame point cloud in the relative laser coordinate system.

[0016] In one embodiment, the S13 includes:

[0017] use The coordinates of the current frame point cloud in the absolute laser coordinate system Convert to the coordinates of the current frame point cloud in the relative laser coordinate system , is the transformation matrix in the depth direction of the coordinate system, represents the rotation matrix, t represents the translation matrix, is the speed of light, The delay time of the current frame.

[0018] In one embodiment, the S2 includes:

[0019] S21: performing voxel downsampling on the current frame point cloud in the relative laser coordinate system to obtain a preprocessed point cloud, extracting fast point feature histogram (FPFH) features corresponding to the preprocessed point cloud, and estimating a surface normal vector of the preprocessed point cloud;

[0020] S22: Based on the RANSAC algorithm, the FPFH features are roughly aligned between point clouds to obtain the first Frame to Rough pose transformation of frames ;

[0021] S23: Based on the point-to-plane ICP algorithm Frame point cloud, Frame point cloud and the rough pose transformation Perform precise registration between laser point clouds to obtain the Frame to Frame-accurate pose transformation .

[0022] In one embodiment, the S3 includes:

[0023] The relative laser coordinate system of the first frame is used as the world coordinate system. Frame point cloud construction relative global map ;in, ;

[0024] Relative global map Downsample the point cloud data in voxels and estimate the surface normal vector of the point cloud after voxel downsampling;

[0025] Utilize the precise pose transformation from the previous frame to the first frame The exact pose transformation from the current frame to the previous frame , get the rough pose transformation from the current frame to the first frame ;

[0026] Based on the point-to-plane ICP algorithm, using the relative global map , current frame point cloud and the rough pose transformation from the current frame to the first frame Calculate the exact pose transformation from the current frame to the first frame .

[0027] In one embodiment, the S4 includes:

[0028] Using the formula Building a relative global map ,set up The point count threshold in is always within the preset range; where N is the point cloud midpoint threshold, is the frame number, Indicates the sequence number of the current frame, Indicates the Frame point cloud Middle The coordinates of a point.

[0029] In one embodiment,

[0030] When the number of points in the point cloud is greater than the threshold N, the starting point cloud is deleted first to keep the number of points in the point cloud within the threshold N; when the number of points in the point cloud is less than or equal to the threshold N, the points in the point cloud are not deleted; specifically expressed as: ;

[0031] in, Indicates the Relative global map constructed by frame point cloud, Indicates that when The point cloud that needs to be deleted when enriching the global map. Used to count the number of elements in a collection.

[0032] In one embodiment, the S5 includes:

[0033] S51: Using formula The relative global pose Restore to the absolute global pose L i ; for The inverse matrix of is the transformation matrix in the depth direction of the current frame; A0 is the transformation matrix in the depth direction of the first frame;

[0034] S52: Use the absolute global pose corresponding to the current frame and relative global map Obtain the enhanced point cloud in the absolute laser coordinate system of the current frame;

[0035] S53: Projecting the enhanced point cloud in the absolute laser coordinate system of the current frame to obtain a depth map of the target scene.

[0036] In one embodiment, the S53 includes:

[0037] Using the formula Projecting the enhanced point cloud in the absolute laser coordinate system of the current frame into the pixel coordinate system to obtain a depth map of the target scene;

[0038] in, Represents the homogeneous coordinates in the pixel coordinate system of the current frame depth map, is the super-resolution projection matrix, is the adjustable resolution factor, its value is greater than or equal to 1, Indicates the coordinates of the enhanced point cloud in the absolute laser coordinate system of the current frame;

[0039] when When it is equal to 1, the projection obtains a dense depth map of the original resolution; when When it is greater than 1, the projection results in a denser depth map with higher resolution.

[0040] According to another aspect of the present invention, a point cloud mapping system for an area array Geiger APD laser radar is provided, comprising:

[0041] The point cloud pre-operation module is used to transform the absolute laser coordinate system of the current frame point cloud in the laser echo signal in the depth direction, so as to convert the current frame point cloud in the laser coordinate system into the current frame point cloud in the relative laser coordinate system. , thereby weakening the influence of large depth values;

[0042] Laser odometry module, used to obtain the precise posture transformation from the current frame to the previous frame using the point cloud of the current frame and the previous frame in the relative laser coordinate system , i is the current frame number, It is a mark of precise posture;

[0043] The global attitude estimation module is used to use the relative laser coordinate system of the first frame point cloud as the world coordinate system, and Frame point cloud builds a relative global map of the previous frame ; ;right Perform point cloud downsampling; use the precise pose transformation from the previous frame to the first frame The exact pose transformation from the current frame to the previous frame , get the rough pose transformation from the current frame to the first frame , is the rough pose identification; using the downsampled 、 and Calculate the exact pose transformation from the current frame to the first frame ;

[0044] Mapping module, used to use the current frame point cloud and the corresponding Build a relative global map of the current frame ,set up The point threshold in is within the preset range;

[0045] The projection module is used to project the relative global pose of the current frame Restore to absolute global pose ; for The inverse matrix of and Obtaining an enhanced point cloud in the current frame absolute laser coordinate system; projecting the enhanced point cloud in the current frame absolute laser coordinate system to obtain a depth map of the target scene.

[0046] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0047] (1) The present invention provides a point cloud mapping method for a planar Geiger APD laser radar. By transforming the coordinate system of the Geiger APD laser point cloud in the depth direction and converting it to a relative coordinate system for registration, the dominant influence of large depth values ​​on posture estimation is weakened, thereby improving the success rate of Geiger APD laser point cloud registration for long-distance imaging; the current frame point cloud is directly registered with the relative global map, which can reduce the cumulative error in the registration process; the number of points in the global map is kept within a preset range to reduce the pressure on computing resources and improve the accuracy of registration, thereby achieving a dense point cloud mapping result for the target scene; the enhanced point cloud is projected and transformed, and finally dense laser imaging of long-distance targets is achieved, thereby solving the technical problem of sparse long-distance imaging in the existing Geiger APD laser mapping algorithm.

[0048] (2) This scheme uses the count value map and the corresponding delay time to represent the absolute depth map of the current frame point cloud, and can obtain the absolute geometric information of the target scene; the absolute depth map of the current frame is back-projected into the corresponding laser coordinate system, and then the absolute laser coordinate system is transformed in the depth direction. Instead of directly using the point cloud in the absolute laser coordinate system for registration, it is transformed into the relative coordinate system for registration, which weakens the dominant influence of large depth values ​​on posture estimation and improves the success rate of Geiger APD laser point cloud registration for long-distance imaging.

[0049] (3) This scheme uses the designed depth-direction transformation operation of the coordinate system of the Geiger APD laser point cloud to subtract half of the product of the delay time of the current frame and the speed of light from the detection distance, thereby weakening the dominant influence of large depth values ​​on posture estimation; the influence of delay time is considered for each frame, which can improve the accuracy of inter-frame registration.

[0050] (4) This scheme performs a rough registration between point clouds on the FPFH features based on the RANSAC algorithm to obtain the first Frame to The frame corresponds to the rough posture transformation; then the ICP algorithm based on point to surface is used to transform the Frame point cloud, The frame point cloud and the rough pose transformation are used to precisely align the laser point clouds. The two registrations can improve the accuracy of inter-frame registration. In addition, the two algorithms have low computational complexity and are well adapted to inter-frame registration.

[0051] (5) This solution is based on the point-to-surface ICP algorithm, using a relative global map , current frame point cloud and the rough pose transformation from the current frame to the first frame Calculate the exact pose transformation from the current frame to the first frame The computational complexity is low and it can adapt well to inter-frame registration. This solution can effectively reduce the cumulative error in the point cloud registration process under the same target scene and improve the registration accuracy.

[0052] (6) This plan sets The point count threshold in is always within the preset range. By assuming that the scene does not change significantly within a certain time range, the number of points in the global map is kept within the specified threshold, which can reduce the pressure on computing resources and improve the accuracy of registration.

[0053] (7) When the number of points in the point cloud is greater than the threshold N, this scheme prioritizes deleting the initial point cloud so that the number of points in the point cloud remains within the threshold N. Deleting the initial point cloud can better adapt to changes in the target scene in the current frame.

[0054] (8) This scheme uses the formula The relative global pose Restore to absolute global pose ; Using the absolute global pose corresponding to the current frame and relative global map Obtain enhanced point cloud in the absolute laser coordinate system of the current frame; be able to obtain dense three-dimensional information of the target scene in the current frame perspective.

[0055] (9) The point cloud super-resolution projection algorithm designed in this scheme projects the enhanced point cloud in the absolute laser coordinate system of the current frame onto a higher resolution depth map, which can achieve super-resolution of the depth map, enhance the detail information of the depth map, and improve the defect of low resolution of the distance image obtained by the Geiger APD detector. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of a point cloud mapping method for a Geiger APD laser radar provided in an embodiment of the present invention.

[0057] Figure 2a and Figure 2b This embodiment of the present invention provides a nearby visible light image of a power plant target.

[0058] Figure 3a and Figure 3b This is the target point cloud mapping result of the power plant provided by the embodiment of the present invention.

[0059] Figure 4 This is the comparison result of the original depth map and the enhanced depth map of the power plant target provided by the embodiment of the present invention.

[0060] Figure 5This is the super-resolution projection result of the power plant target point cloud provided by an embodiment of the present invention.

[0061] Figure 6 This is a block diagram of the area array Geiger APD lidar point cloud mapping system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0063] Figure 1 This is a flow chart of the point cloud mapping method for the area array Geiger APD laser radar provided by the embodiment of the present invention. Figure 1 , the point cloud mapping method of the area array Geiger APD laser radar in this embodiment is described in detail, and the method includes S1-S5.

[0064] In this embodiment, the data collected by the airborne Geiger APD laser radar about 3500m away from a power plant is used as the implementation data. The visible light image of the power plant target at a close distance is as follows: Figure 2a and Figure 2b As shown, the red box is the specific target.

[0065] S1, transform the absolute laser coordinate system of the current frame point cloud in the laser echo signal in the depth direction to convert the current frame point cloud in the laser coordinate system into the current frame point cloud in the relative laser coordinate system , thereby weakening the impact of large depth values.

[0066] According to an embodiment of the present invention, S1 includes S11-S13.

[0067] In S11 , a count value map of the current frame in the laser echo signal is obtained, and an absolute depth map of the current frame is calculated using the count value map and the corresponding delay time.

[0068] In this embodiment, the accumulated 400-frame count value graph and corresponding delay time of the Geiger APD laser radar after denoising are obtained, and the resolution of the numerical graph is , the absolute depth map is calculated by the count value map and the delay time, and the obtained resolution is Absolute depth map of It can be expressed as:

[0069] ;

[0070] in, A graph showing the count values ​​output by the lidar, It is a coefficient, and its value is related to the multi-frame cumulative imaging of the laser radar. Here it is 0.18. represents the speed of light, Indicates the delay time, in units of , A binary mask representing the count value map, Pixels greater than 0 in Set the center pixel to 1 and the rest of the pixels to 0.

[0071] In S12, the absolute depth map of the current frame is back-projected and transformed into the corresponding laser coordinate system to obtain the point cloud of the current frame in the absolute laser coordinate system.

[0072] In this embodiment, the absolute depth map obtained in S11 is back-projected and transformed into the corresponding laser coordinate system to obtain a point cloud in the absolute laser coordinate system. The transformation method is expressed as follows:

[0073] ;

[0074] in, Represents the absolute depth map Homogeneous pixel coordinates in the pixel coordinate system of . represents the intrinsic parameter matrix of the laser detector, The value is 4000. The value is 4000. The value is 31.5, The value is 31.5. Represents the point cloud coordinates of the absolute depth map back-projected to the absolute laser coordinate system.

[0075] In S13 , the absolute laser coordinate system is transformed in the depth direction to convert the current frame point cloud in the absolute laser coordinate system into the current frame point cloud in the relative laser coordinate system.

[0076] In one embodiment, the point cloud in the absolute laser coordinate system obtained in S12 is transformed in the depth direction to obtain a point cloud in the relative coordinate system. The transformation method is expressed as follows:

[0077] ;

[0078] in, Represents the homogeneous coordinates of the current frame point cloud in the absolute laser coordinate system, is the transformation matrix in the depth direction of the coordinate system, represents the rotation matrix, represents the translation matrix, Represents the homogeneous coordinates of the current frame point cloud relative to the laser coordinate system.

[0079] S2, using the point cloud of the current frame and the previous frame in the relative laser coordinate system to obtain the precise posture transformation from the current frame to the previous frame , i is the current frame number, It is a mark of precise posture;

[0080] According to an embodiment of the present invention, S2 includes S21-S23.

[0081] In S21, voxel downsampling is performed on the current frame point cloud in the relative laser coordinate system to obtain a preprocessed point cloud, fast point feature histogram FPFH features corresponding to the preprocessed point cloud are extracted, and a surface normal vector of the preprocessed point cloud is estimated.

[0082] In one embodiment, the Geiger APD laser point cloud in the relative coordinate system obtained in S1 is downsampled, with a voxel size of 1. A fast point feature histogram feature point is generated for the downsampled point cloud, with a feature radius set to 5. Similarly, a normal vector is estimated for the downsampled point cloud, with a normal vector radius set to 2.

[0083] In S22, the FPFH features are roughly aligned between point clouds based on the RANSAC algorithm to obtain the first Frame to Rough pose transformation of frames .

[0084] In one embodiment, the point cloud pre-processed in S21 is subjected to rough registration between point cloud frames. Frame point cloud to the first Rough pose transformation of frame point cloud Expressed as:

[0085] ;

[0086] in, Represents the RANSAC algorithm mapping function, and the distance threshold of the RANSAC algorithm is set to 1. Indicates the extraction of point cloud fast point feature histogram FPFH feature operation, , After preprocessing, -1 frame point cloud, After preprocessing, Frame point cloud.

[0087] In S23, the point-to-surface ICP algorithm is used to Frame point cloud, Frame point cloud and the rough pose transformation Perform precise registration between laser point clouds to obtain the Frame to Frame-accurate pose transformation .

[0088] In one embodiment, the rough pose transformation between point cloud frames obtained in S22 is used as the initial matrix. The point-to-plane ICP algorithm is used to achieve precise registration between point clouds, and a more accurate pose transformation matrix between frames is obtained. Frame to Accurate pose transformation of frame point cloud Expressed as:

[0089]

[0090] Where ICP represents the mapping function of the ICP algorithm from point to surface, and the maximum corresponding point distance of the ICP algorithm is set to 5.

[0091] S3, taking the relative laser coordinate system of the first frame point cloud as the world coordinate system, using the first Frame point cloud builds a relative global map of the previous frame ; ;right Perform point cloud downsampling; use the precise pose transformation from the previous frame to the first frame The exact pose transformation from the current frame to the previous frame , get the rough global pose from the current frame to the first frame , is the rough pose identification; using the downsampled 、 and Calculate the exact pose transformation from the current frame to the first frame .

[0092] In one embodiment, similar to S21, the point cloud data relative to the global map is downsampled, and the voxel size is 1. Similarly, the normal vector is estimated for the downsampled point cloud, and the normal vector radius is set to 2.

[0093] In one embodiment, the precise pose transformation from the previous frame to the first frame is used The exact pose transformation from the current frame to the previous frame , get the rough pose transformation from the current frame to the first frame , which is used as the initial pose of the point-to-surface ICP algorithm, and the rough pose transformation from the current frame to the first frame Expressed as:

[0094] ;

[0095] Use the estimated normal vector for precise registration to obtain the precise posture transformation from the current frame to the first frame Expressed as:

[0096] ;

[0097] in, Indicated as 0th~ Relative global map constructed by the frame.

[0098] S4, using the current frame point cloud and the corresponding Build a relative global map of the current frame ,set up The point threshold in is within the preset range.

[0099] In one embodiment, a relative global map of the power plant target is constructed. The relative laser coordinate system of the 0th frame is used as the world coordinate system. Relative global map constructed from frame point cloud Expressed as:

[0100] ;

[0101] in, Indicates the Frame point cloud Middle The coordinates of a point.

[0102] As the global map of the power plant target continues to be enriched, when the number of points in the point cloud is greater than the threshold N, the initial point cloud is deleted first to keep the number of points in the point cloud within the threshold N; when the number of points in the point cloud is less than or equal to the threshold N, the points in the point cloud are not deleted. Here, N is 8000. This can be expressed as: ;

[0103] in, Indicates the A global map constructed from frame point clouds, Indicates that when The point cloud that needs to be deleted when enriching the global map. Figure 3a and Figure 3b shown.

[0104] S5, the relative global pose of the current frame Restore to absolute global pose ; for The inverse matrix of and Obtain an enhanced point cloud in the absolute laser coordinate system of the current frame; and project the enhanced point cloud in the absolute laser coordinate system of the current frame to obtain a dense depth map of the target scene.

[0105] According to an embodiment of the present invention, S5 includes S51-S53.

[0106] S51, using the formula The relative global pose Restore to absolute global pose ; for The inverse matrix of is the transformation matrix in the depth direction of the current frame; A0 is the transformation matrix in the depth direction of the first frame.

[0107] S52, using the absolute global pose corresponding to the current frame and relative global map Obtain the enhanced point cloud in the absolute laser coordinate system of the current frame.

[0108] In one embodiment, the enhanced point cloud in the absolute laser coordinate system of the current frame is obtained, which is expressed as:

[0109] ;

[0110] in, Represents the homogeneous coordinates of the point cloud relative to the global map coordinates, Represents the homogeneous coordinates of the enhanced point cloud in the absolute laser coordinate system of the current frame.

[0111] S53 , projecting the enhanced point cloud in the absolute laser coordinate system of the current frame to obtain a depth map of the target scene.

[0112] In one embodiment, using the formula Projecting the enhanced point cloud in the absolute laser coordinate system of the current frame into the pixel coordinate system to obtain a depth map of the target scene;

[0113] in, Represents the homogeneous coordinates in the pixel coordinate system of the current frame depth map, is the super-resolution projection matrix, is the adjustable resolution factor, its value is greater than or equal to 1, Indicates the coordinates of the enhanced point cloud in the absolute laser coordinate system of the current frame.

[0114] when When it is equal to 1, the projection obtains a dense depth map of the original resolution; when When it is greater than 1, the projection results in a denser depth map with higher resolution.

[0115] During the projection process, when there are multiple depth values ​​at the same pixel coordinate, the smallest depth value is selected as the actual depth value at this point in the depth map. Figure 4The schematic diagram of the super-resolution projection result of the power plant target point cloud is shown in Figure 5 As shown, , that is, the resolution of the depth map changes from Increase to .

[0116] Figure 6 This is a block diagram of the area array Geiger APD laser radar point cloud mapping system provided by an embodiment of the present invention. Figure 6 The area array Geiger APD laser radar point cloud mapping system 600 includes a point cloud pre-operation module 610, a laser odometry module 620, a global pose estimation module 630, a mapping module 640 and a projection module 650.

[0117] The point cloud pre-operation module 610 executes S1, for example, to transform the absolute laser coordinate system of the current frame point cloud in the laser echo signal in the depth direction, so as to convert the current frame point cloud in the laser coordinate system into the current frame point cloud in the relative laser coordinate system. , thereby weakening the impact of large depth values.

[0118] The laser odometry module 620 executes S2, for example, to obtain the precise posture transformation from the current frame to the previous frame using the point cloud of the current frame and the previous frame in the relative laser coordinate system. , i is the current frame number, It is a symbol of precise posture.

[0119] The global pose estimation module 630 executes S3, for example, to use the relative laser coordinate system of the first frame point cloud as the world coordinate system, and Frame point cloud builds a relative global map of the previous frame ; ;right Perform point cloud downsampling; use the precise pose transformation from the previous frame to the first frame The exact pose transformation from the current frame to the previous frame , get the rough global pose from the current frame to the first frame , is the rough pose identification; using the downsampled 、 and Calculate the exact pose transformation from the current frame to the first frame .

[0120] The mapping module 640 executes S4, for example, to use the current frame point cloud and the corresponding Build a relative global map of the current frame ,set up The point threshold in is within the preset range.

[0121] The projection module 650 executes S5, for example, to convert the relative global pose of the current frame into Restore to absolute global pose ; for The inverse matrix of and Obtaining an enhanced point cloud in the current frame absolute laser coordinate system; projecting the enhanced point cloud in the current frame absolute laser coordinate system to obtain a depth map of the target scene.

[0122] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A point cloud mapping method for an area array Geiger APD laser radar, characterized in that: include: S1: Perform depth transformation on the absolute laser coordinate system of the current frame point cloud in the laser echo signal to convert the current frame point cloud in the laser coordinate system into the current frame point cloud in the relative laser coordinate system. , thereby weakening the influence of large depth values; among them, using The coordinates of the current frame point cloud in the absolute laser coordinate system Convert to the coordinates of the current frame point cloud in the relative laser coordinate system , is the transformation matrix in the depth direction of the coordinate system, represents the rotation matrix, t represents the translation matrix, is the speed of light, is the delay time of the current frame; S2: Use the point cloud of the current frame and the previous frame in the relative laser coordinate system to obtain the precise posture transformation from the current frame to the previous frame , i is the current frame number, It is a mark of precise posture; S3: Take the relative laser coordinate system of the first frame point cloud as the world coordinate system, and use the first Frame point cloud builds a relative global map of the previous frame ; ;right Perform point cloud downsampling; use the precise pose transformation from the previous frame to the first frame The exact pose transformation from the current frame to the previous frame , get the rough pose estimate from the current frame to the first frame , is the rough pose identification; using the downsampled 、 and Calculate the exact pose transformation from the current frame to the first frame ; S4: Using the current frame point cloud and the corresponding Build a relative global map of the current frame ,set up The point threshold in is within the preset range; S5: The relative global pose of the current frame Restore to absolute global pose ; for The inverse matrix of and Obtaining an enhanced point cloud in the absolute laser coordinate system of the current frame; projecting the enhanced point cloud in the absolute laser coordinate system of the current frame to obtain a dense depth map of the target scene; Among them, using the formula Projecting the enhanced point cloud in the absolute laser coordinate system of the current frame into the pixel coordinate system to obtain a depth map of the target scene; Represents the homogeneous coordinates in the pixel coordinate system of the current frame depth map, is the super-resolution projection matrix, is the adjustable resolution factor, its value is greater than or equal to 1, Indicates the coordinates of the enhanced point cloud in the absolute laser coordinate system of the current frame; when When it is equal to 1, the projection obtains a dense depth map of the original resolution; when When it is greater than 1, the projection results in a denser depth map with higher resolution.

2. The point cloud mapping method of the area array Geiger APD laser radar according to claim 1, characterized in that: Said S1 comprises: S11: Obtaining a count value map of a current frame in the laser echo signal, and calculating an absolute depth map of the current frame using the count value map and the corresponding delay time; S12: Back-project the absolute depth map of the current frame into the corresponding laser coordinate system to obtain the point cloud of the current frame in the absolute laser coordinate system; S13: performing depth-wise transformation on the absolute laser coordinate system to convert the current frame point cloud in the laser coordinate system into the current frame point cloud in the relative laser coordinate system.

3. The point cloud mapping method for the area array Geiger APD laser radar according to claim 1, characterized in that: The S2 includes: S21: performing voxel downsampling on the current frame point cloud in the relative laser coordinate system to obtain a preprocessed point cloud, extracting fast point feature histogram (FPFH) features corresponding to the preprocessed point cloud, and estimating a surface normal vector of the preprocessed point cloud; S22: Based on the RANSAC algorithm, the FPFH features are roughly aligned between point clouds to obtain the first Frame to Rough pose transformation of frames ; S23: Based on the point-to-plane ICP algorithm Frame point cloud, Frame point cloud and the rough pose transformation Perform precise registration between laser point clouds to obtain the Frame to Frame-accurate pose transformation .

4. The point cloud mapping method for the area array Geiger APD laser radar according to claim 1, characterized in that: The S3 includes: The relative laser coordinate system of the first frame is used as the world coordinate system. Frame point cloud construction relative global map ;in, ; Relative global map Downsample the point cloud data in voxels and estimate the surface normal vector of the point cloud after voxel downsampling; Utilize the precise pose transformation from the previous frame to the first frame The exact pose transformation from the current frame to the previous frame , get the rough pose estimate from the current frame to the first frame ; Based on the point-to-plane ICP algorithm, using the relative global map , current frame point cloud and the rough pose estimation from the current frame to the first frame Calculate the exact pose transformation from the current frame to the first frame .

5. The point cloud mapping method for the area array Geiger APD laser radar according to claim 1, characterized in that: The S4 includes: Using the formula Building a relative global map ,set up The point count threshold in is always within the preset range; where N is the point cloud midpoint threshold, is the frame number, Indicates the sequence number of the current frame, Indicates the Frame point cloud Middle The coordinates of a point.

6. The point cloud mapping method of the area array Geiger APD laser radar according to claim 5, characterized in that: Said S4 further comprises: When the number of points in the point cloud is greater than the threshold N, the starting point cloud is deleted first to keep the number of points in the point cloud within the threshold N; when the number of points in the point cloud is less than or equal to the threshold N, the points in the point cloud are not deleted; specifically expressed as: ； in, Indicates the Relative global map constructed by frame point cloud, Indicates that when The point cloud that needs to be deleted when enriching the global map. Used to count the number of elements in a collection.

7. The point cloud mapping method for area array Geiger APD laser radar according to claim 1, characterized in that: The S5 includes: S51: Using formula The relative global pose Restore to absolute global pose ; for The inverse matrix of is the transformation matrix in the depth direction of the current frame; is the transformation matrix in the depth direction of the first frame; S52: Use the absolute global pose corresponding to the current frame and relative global map Obtain the enhanced point cloud in the absolute laser coordinate system of the current frame; S53: Projecting the enhanced point cloud in the absolute laser coordinate system of the current frame to obtain a dense depth map of the target scene.

8. A point cloud mapping system for an area array Geiger APD laser radar, characterized in that: A point cloud mapping method for executing the area array Geiger APD laser radar according to any one of claims 1 to 7, comprising: The point cloud pre-operation module is used to transform the absolute laser coordinate system of the current frame point cloud in the laser echo signal in the depth direction, so as to convert the current frame point cloud in the laser coordinate system into the current frame point cloud in the relative laser coordinate system. , thereby weakening the influence of large depth values; Laser odometry module, used to obtain the precise posture transformation from the current frame to the previous frame using the point cloud of the current frame and the previous frame in the relative laser coordinate system , i is the current frame number, For the identification of precise posture; The global attitude estimation module is used to use the relative laser coordinate system of the first frame point cloud as the world coordinate system, and Frame point cloud builds a relative global map of the previous frame ; ;right Perform point cloud downsampling; use the precise pose transformation from the previous frame to the first frame The exact pose transformation from the current frame to the previous frame , get the rough pose transformation from the current frame to the first frame , is the rough pose identification; using the downsampled 、 and Calculate the exact pose transformation from the current frame to the first frame ; Mapping module, used to use the current frame point cloud and the corresponding Build a relative global map of the current frame ,set up The point threshold in is within the preset range; The projection module is used to project the relative global pose of the current frame Restore to absolute global pose ; for The inverse matrix of and Obtain an enhanced point cloud in the absolute laser coordinate system of the current frame; and project the enhanced point cloud in the absolute laser coordinate system of the current frame to obtain a dense depth map of the target scene.

Images