CN114485608A - Local point cloud rapid registration method for high-precision map making - Google Patents
Local point cloud rapid registration method for high-precision map making Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3807—Creation or updating of map data characterised by the type of data
- G01C21/3815—Road data
- G01C21/3822—Road feature data, e.g. slope data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3807—Creation or updating of map data characterised by the type of data
- G01C21/3811—Point data, e.g. Point of Interest [POI]
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Abstract
The invention provides a local point cloud rapid registration method for high-precision map making, which comprises the following steps: s1: extracting points in a track point range from a point cloud plane extraction source and a target point cloud, acquiring plane parameters by adopting a random sampling consistency algorithm, obtaining a transformation parameter T1 from the plane parameters of the source point cloud and the target point cloud, and transforming the source point cloud by using the transformation parameter T1; s2: projecting the transformed source point cloud and the target power supply into a plane, and generating projection drawings Is and Ig; s3: and in a given angle range, rotating the projection drawing Is at an interval to obtain Is ', obtaining a matching position and a related coefficient value by adopting a template matching algorithm through the Is' and the Ig, finally comparing the related coefficient values under all rotation angles, and obtaining an optimal configuration position and a rotation angle to obtain a transformation parameter T2. By using the scheme of the invention, the time is consumed within 200ms, and the total efficiency improvement is very large for a large amount of road point clouds needing to be registered.
Description
Technical Field
The invention relates to the technical field of point cloud data processing, in particular to a local point cloud rapid registration method for high-precision map making.
Background
In the field of high-precision electronic maps, data acquired by each road usually comprises laser point cloud data, and road elements may not be completely acquired at one time and need to be updated from time to time, so that data acquired by one road often has multiple times, and due to gps, inertial navigation precision and point cloud sensor coordinate calculation, data acquired by multiple times are often not completely matched, and a registration algorithm is needed to match point cloud data acquired by multiple times. And at different moments, errors generated by gps, inertial navigation and the like are different, so that point cloud is required to be divided into a plurality of local areas for registration, and as elements are required to be extracted from the point cloud, the point cloud acquisition range is large, the density of the point cloud is also large, the existing mature point cloud registration algorithm is basically divided into icp, ndt and a variant algorithm thereof, iteration is required, and high requirements are required on initial values, so that for a large amount of point clouds, the convergence standard cannot be reached after iteration for many times under certain conditions, and the registration time is too long. Some point cloud registration methods based on deep learning also exist at present, and have good accuracy and recall rate, but the methods need a large number of samples for learning, have high requirements on the performance of an operation platform, are difficult to adapt to point clouds of hundreds of thousands of orders of magnitude, and basically avoid a large number of nearest search calculations and data preprocessing algorithms, so that the efficiency is not high.
Disclosure of Invention
The present invention provides a method for fast registration of local point clouds for high precision mapping that overcomes or at least partially solves the above mentioned problems.
According to a first aspect of the present invention, a local point cloud fast registration method for high-precision mapping is provided, which includes the following steps:
s1: extracting points in a track point range from a point cloud plane extraction source and a target point cloud, acquiring plane parameters by adopting a random sampling consistency algorithm, obtaining a transformation parameter T1 from the plane parameters of the source point cloud and the target point cloud, and transforming the source point cloud by using the transformation parameter T1;
s2: projecting the transformed source point cloud and the target power supply into a plane, and generating projection drawings Is and Ig;
s3: and in a given angle range, rotating the projection drawing Is at an interval to obtain Is ', obtaining a matching position and a related coefficient value by adopting a template matching algorithm through the Is' and the Ig, finally comparing the related coefficient values under all rotation angles, and obtaining an optimal configuration position and a rotation angle to obtain a transformation parameter T2.
S4: and (5) integrating the transformation parameters T1 and T2 to obtain registration parameters of the source point cloud and the target point cloud, and finishing registration.
Based on the technical scheme of the invention, the following improvements can be made:
optionally, the step S1 includes:
s11: reading point cloud data, dividing the point cloud data into a plurality of grids according to areas, taking point clouds needing to be registered in the grids, target point clouds and collected track points, wherein the point clouds needing to be registered are marked as PTSs, the target point clouds are marked as PTSt, offset is set, the point cloud origin is PT, and the range of the target point clouds is larger than that of the source point clouds in the point cloud taking process;
s22: establishing KDTree for point cloud to be registered, searching and collecting all point clouds needing to be registered in a given radius of a track point, extracting a plane by using a random sampling consistency algorithm, and setting normal direction and angle tolerance range of the plane to obtain plane parameters (Sx, Sy, Sz and Sd); and further extracting a target point cloud plane to obtain plane parameters (Gx, Gy, Gz and Gd), obtaining a rotation matrix by using the normal vectors (Sx, Sy and Sz) and the normal vectors (Gx, Gy and Gz), obtaining a final transformation parameter T1 by combining the intercept Sd and the Td, transforming the PTSs by using the parameters, setting a certain point of the plane (Gx, Gy, Gz and Gd) close to the original point as a new original point, and obtaining PTS's and PTS' T after translation, wherein the translation parameter is recorded as D1.
Optionally, the step S2 includes:
projecting PTS's onto a plane (Gx, Gy, Gz, Gd), setting a resolution on the plane by taking an original point as a center, generating a picture from the point clouds, enabling a plurality of point clouds to fall in the same pixel, and averaging the pixel values with the intensity of the plurality of point clouds to obtain a projection picture Is;
and projecting the PTS't onto a plane (Sx, Sy, Sz, Sd), setting a resolution on the plane by taking an original point as a center, generating a picture of the target point cloud, enabling a plurality of target point clouds to fall in the same pixel, and taking the average value of the intensities of the plurality of target point clouds according to the value of the pixel to obtain a projection picture Ig.
Optionally, in the step S4, after the final transformation parameters T2 and T1 are obtained and the registration of the source point cloud and the target point cloud is completed, the steps S1 to S4 are repeated to obtain the registration of other point clouds.
Compared with two pieces of point clouds based on icp, ndt and the variety thereof, the local point cloud rapid registration method for high-precision map making, provided by the invention, registers about 40 ten thousand points, and consumes about the second order of time on average according to the characteristics of different point clouds, but by using the scheme of the invention, the time is consumed within 200ms, and the total efficiency is greatly improved for a large amount of road point clouds needing registration.
Drawings
Fig. 1 is a flowchart of a local point cloud fast registration method for high-precision mapping according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of a local point cloud fast registration method for high-precision mapping according to an embodiment of the present invention, and as shown in fig. 1, a local point cloud fast registration method for high-precision mapping is provided, which includes the following steps:
s1: extracting points in a track point range from a point cloud plane extraction source and a target point cloud, acquiring plane parameters by adopting a random sampling consistency algorithm, obtaining a transformation parameter T1 from the plane parameters of the source point cloud and the target point cloud, and transforming the source point cloud by using the transformation parameter T1;
s2: projecting the transformed source point cloud and the target power supply into a plane, and generating projection drawings Is and Ig;
s3: and in a given angle range, rotating the projection drawing Is at an interval to obtain Is ', obtaining a matching position and a related coefficient value by adopting a template matching algorithm through the Is' and the Ig, finally comparing the related coefficient values under all rotation angles, and obtaining an optimal configuration position and a rotation angle to obtain a transformation parameter T2.
S4: and (5) integrating the transformation parameters T1 and T2 to obtain registration parameters of the source point cloud and the target point cloud, and finishing registration.
Based on the technical scheme of the invention, the following improvements can be made:
wherein the step S1 includes:
s11: reading point cloud data, dividing the point cloud data into a plurality of grids according to areas, taking point clouds needing to be registered in the grids, target point clouds and collected track points, wherein the point clouds needing to be registered are marked as PTSs, the target point clouds are marked as PTSt, offset is set, the point cloud origin is PT, and the range of the target point clouds is larger than that of the source point clouds in the point cloud taking process;
s22: establishing KDTree for point cloud to be registered, searching and collecting all point clouds needing to be registered in a given radius of a track point, extracting a plane by using a random sampling consistency algorithm, and setting normal direction and angle tolerance range of the plane to obtain plane parameters (Sx, Sy, Sz and Sd); and further extracting a target point cloud plane to obtain plane parameters (Gx, Gy, Gz and Gd), obtaining a rotation matrix by using the normal vectors (Sx, Sy and Sz) and the normal vectors (Gx, Gy and Gz), obtaining a final transformation parameter T1 by combining the intercept Sd and the Td, transforming the PTSs by using the parameters, setting a certain point of the plane (Gx, Gy, Gz and Gd) close to the original point as a new original point, and obtaining PTS's and PTS' T after translation, wherein the translation parameter is recorded as D1.
Wherein the step S2 includes:
projecting PTS's onto a plane (Gx, Gy, Gz, Gd), setting a resolution on the plane by taking an original point as a center, generating a picture from the point clouds, enabling a plurality of point clouds to fall in the same pixel, and averaging the pixel values with the intensity of the plurality of point clouds to obtain a projection picture Is;
and projecting the PTS't onto a plane (Sx, Sy, Sz, Sd), setting a resolution on the plane by taking an original point as a center, generating a picture of the target point cloud, enabling a plurality of target point clouds to fall in the same pixel, and taking the average value of the intensities of the plurality of target point clouds according to the value of the pixel to obtain a projection picture Ig.
In step S4, the final transformation parameters T2 and T1 are obtained, and after the registration of the source point cloud and the target point cloud is completed, steps S1 to S4 are repeated to obtain the registration of other point clouds.
It can be understood that, the present embodiment provides a local point cloud fast registration method for high-precision mapping, compared to registering two pieces of point clouds of about 40 ten thousand points based on icp and ndt and their variants, the time consumption is about the order of seconds on average according to different point cloud characteristics, and with the solution of the present invention, the time consumption is within 200ms, and the total efficiency improvement is very large for a large amount of road point clouds requiring registration.
Specifically, as an example, the method comprises the following steps:
step 1: reading point cloud data, dividing the point cloud data into a plurality of grids according to areas, taking point clouds PTSs and target point clouds PTSt which need to be registered in the grids, and collecting track points PT ((above the road surface), setting offset to enable the point cloud origin to be PT, namely PT coordinates to be (0, 0, 0), and enabling the range of the target point cloud to be 2m larger than that of the source point cloud when the point cloud is taken.
Step 2: establishing KDTree for the PTSs, searching all the PTSs within the radius of 5m of a trace point PT, extracting a plane by using a random sampling consistency (RANSAC) algorithm, setting the normal direction (0, 0 and 1) of the plane, and obtaining plane parameters (Sx, Sy, Sz and Sd) with the angle tolerance range of 20 degrees; extracting a target point cloud plane in the same way to obtain plane parameters (Gx, Gy, Gz and Gd), obtaining a rotation matrix (the rotation axis is the normal direction of the plane formed by the two normal vectors and the rotation angle is the included angle of the two normal vectors) by the normal vectors (Sx, Sy and Sz) and the normal vectors (Gx, Gy and Gz), obtaining a final transformation parameter T1 by combining the intercept Sd and Td, transforming the PTSs by using the parameter, and setting a certain point of the plane (Gx, Gy, Gz and Gd) close to the original point as a new original point. And obtaining PTS's and PTS't after translation, and recording translation parameters as D1.
And step 3: and (3) projecting the PTS's onto a plane (Gx, Gy, Gz and Gd), generating a picture from the point clouds on the plane by taking an original point as a center and adopting a resolution of (5cm and 5cm), enabling a plurality of point clouds to fall in the same pixel, taking an average value of the intensity of the plurality of point clouds to obtain a projection picture Is, and projecting the projection picture to obtain a picture Ig.
And 4, step 4: and rotating Is within a range of (-5 degrees to 5 degrees) at a resolution of 0.1 degrees to obtain I's, and matching the I's and the Ig by a sampling template matching algorithm to obtain an optimal matching position (Ix, Iy) and a correlation coefficient. And (4) converting the transformation parameter T2 by taking the best matching position and the rotation angle with the maximum relation number in all the rotation angles.
And 5: and synthesizing the transformation parameters obtained in the previous step to obtain final transformation parameters T2 × T1, completing registration of the source point cloud and the target point cloud, and then repeating the same steps to obtain registration between other point cloud pairs.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (4)
1. A local point cloud rapid registration method for high-precision map making is characterized by comprising the following steps:
s1: extracting points in a track point range from a point cloud plane extraction source and a target point cloud, acquiring plane parameters by adopting a random sampling consistency algorithm, obtaining a transformation parameter T1 from the plane parameters of the source point cloud and the target point cloud, and transforming the source point cloud by using the transformation parameter T1;
s2: projecting the transformed source point cloud and the target power supply into a plane, and generating projection drawings Is and Ig;
s3: rotating the projection drawing Is at an interval within a given angle range to obtain Is ', obtaining a matching position and a related coefficient value by adopting a template matching algorithm with the Ig through the Is', finally comparing the related coefficient values under all rotation angles, and obtaining an optimal configuration position and a rotation angle to obtain a transformation parameter T2;
s4: and (5) integrating the transformation parameters T1 and T2 to obtain registration parameters of the source point cloud and the target point cloud, and finishing registration.
2. The method for fast registration of local point clouds in high-precision mapping according to claim 1, wherein the step S1 includes:
s11: reading point cloud data, dividing the point cloud data into a plurality of grids according to areas, taking point clouds needing to be registered in the grids, target point clouds and collected track points, wherein the point clouds needing to be registered are marked as PTSs, the target point clouds are marked as PTSt, offset is set, the point cloud origin is PT, and the range of the target point clouds is larger than that of the source point clouds in the point cloud taking process;
s22: establishing KDTree for point cloud to be registered, searching and collecting all point clouds needing to be registered in a given radius of a track point, extracting a plane by using a random sampling consistency algorithm, and setting normal direction and angle tolerance range of the plane to obtain plane parameters (Sx, Sy, Sz and Sd); and further extracting a target point cloud plane to obtain plane parameters (Gx, Gy, Gz and Gd), obtaining a rotation matrix by using the normal vectors (Sx, Sy and Sz) and the normal vectors (Gx, Gy and Gz), obtaining a final transformation parameter T1 by combining the intercept Sd and the Td, transforming the PTSs by using the parameters, setting a certain point of the plane (Gx, Gy, Gz and Gd) close to the original point as a new original point, and obtaining PTS's and PTS' T after translation, wherein the translation parameter is recorded as D1.
3. The local point cloud fast registration method for high-precision mapping according to claim 2, wherein the step S2 includes:
projecting PTS's onto a plane (Gx, Gy, Gz, Gd), setting a resolution on the plane by taking an original point as a center, generating a picture from the point clouds, enabling a plurality of point clouds to fall in the same pixel, and averaging the pixel values with the intensity of the plurality of point clouds to obtain a projection picture Is;
and projecting the PTS't onto a plane (Sx, Sy, Sz, Sd), setting a resolution on the plane by taking an original point as a center, generating a picture of the target point cloud, enabling a plurality of target point clouds to fall in the same pixel, and taking the average value of the intensities of the plurality of target point clouds according to the value of the pixel to obtain a projection picture Ig.
4. The method as claimed in claim 3, wherein in step S4, after obtaining final transformation parameters T2 and T1 and completing the registration of the source point cloud and the target point cloud, repeating steps S1-S4 to obtain the registration of other point clouds.
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