CN112767457A - Principal component analysis-based plane point cloud matching method and device - Google Patents

Principal component analysis-based plane point cloud matching method and device Download PDF

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CN112767457A
CN112767457A CN202110097200.3A CN202110097200A CN112767457A CN 112767457 A CN112767457 A CN 112767457A CN 202110097200 A CN202110097200 A CN 202110097200A CN 112767457 A CN112767457 A CN 112767457A
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吉长江
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Beijing Moviebook Technology Corp ltd
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Abstract

The application discloses a plane point cloud matching method and device based on principal component analysis, wherein the method comprises the following steps: extracting main components of two characteristic point clouds to obtain main directions of the two characteristic point clouds; performing initial registration on the principal directions of the two feature point clouds by using a PCA principal component analysis method; and intercepting pixels of unit length around the two feature point clouds, inputting the pixels into a CNN neural network for comparison, and determining a matching result of the two feature point clouds. The device comprises: the device comprises a principal component analysis module, an initial registration module and a convolutional neural network module. The method and the device have the advantages that the registration processing capability of the point cloud data with large data volume is strong, and the algorithm time and space complexity is controllable. By utilizing the feature extraction of PCA and the pattern matching of CNN, the high-efficiency and high-resolution point cloud learning is realized.

Description

Principal component analysis-based plane point cloud matching method and device
Technical Field
The application relates to the technical field of computer data processing, in particular to a plane point cloud matching method and device based on principal component analysis.
Background
In the fields of computer vision and SLAM (Simultaneous Localization and Mapping), local point clouds need to be registered to obtain complete three-dimensional point clouds due to the fact that point cloud data collected by a depth camera is incomplete, rotational, translational and dislocated and the like, and the point collection obtained from all visual angles is combined to a uniform coordinate system to form the registration of the complete three-dimensional point cloud data is always a research hotspot and becomes a primary task of many applications including object pose estimation, scene three-dimensional reconstruction and visual SLAM.
The existing method for registering the geometrical characteristics of the point cloud comprises the following steps of 1) constructing a histogram of geometrical characteristics, for example, describing the space difference between a query point and a neighborhood point through a multidimensional histogram based on a calculation method of a fast point characteristic histogram (FPFH); 2) and repeatedly selecting the corresponding relation Point pair by a least square method based on the ICP (Iterative Closest Point) of distance measurement, and calculating the optimal rigid body transformation until the convergence precision requirement of correct registration is met. The method is easy to generate wrong corresponding points for the point cloud to be matched with large angle and scale change, or the point cloud to be matched is trapped in local optimization to cause registration failure. In addition, there are also registration algorithms based on geometric shapes, such as 4PCS (4-Points consistency Sets, point cloud rough registration method) which establishes a correspondence by finding Congruent and coplanar quadrilaterals on two point Sets, but for point Sets with small overlapping areas, it is often difficult to find a correspondence. The Super4PCS algorithm based on the 4PCS method improves the problem, but the time complexity is far higher than that of the similar algorithm. The algorithm can obtain better effect on the registration of the point cloud data with simple scene; however, in an actual application scenario, if SLAM deals with inter-frame registration and solves the problem of ring closure, the point cloud data acquired from the depth camera often has the situations of only partial point cloud registration, large rotation and translation transformation angle, and the like, and these algorithms often have difficulty in obtaining a relatively ideal effect.
Disclosure of Invention
It is an object of the present application to overcome the above problems or to at least partially solve or mitigate the above problems.
According to one aspect of the application, a plane point cloud matching method based on principal component analysis is provided, and comprises the following steps:
extracting main components of two characteristic point clouds to obtain main directions of the two characteristic point clouds;
performing initial registration on the principal directions of the two feature point clouds by using a PCA principal component analysis method;
and intercepting pixels of unit length around the two feature point clouds, inputting the pixels into a CNN neural network for comparison, and determining a matching result of the two feature point clouds.
Preferably, the method further comprises:
when the two feature point clouds are correctly matched, continuing to calculate the similarity of the two feature point clouds;
when the two feature point clouds are not correctly matched, outputting a matching result: the two feature point clouds are dissimilar.
Preferably, the initial registration of the principal directions of the two feature point clouds using PCA principal component analysis comprises:
carrying out characteristic value decomposition on the covariance information of the two characteristic point clouds to obtain characteristic vector matrixes corresponding to the two characteristic point clouds;
and performing matrix transformation on the feature vector matrix to register the main directions of the two feature point clouds.
Preferably, performing eigenvalue decomposition on the covariance information of the two feature point clouds to obtain the feature vector matrix corresponding to the two feature point clouds comprises:
respectively calculating the centroid coordinates of the source point cloud X and the target point cloud Y, and expressing the centroid coordinates as xmean and ymean;
respectively calculating covariance matrixes of a source point cloud X and a target point cloud Y, and expressing the covariance matrixes as Xcovar and Ycovar;
respectively carrying out eigenvalue decomposition on the two obtained covariance matrixes to obtain two corresponding eigenvector matrixes expressed as Xeigen and Yeigen,
wherein X ═ { X ═ Xi∈R3|i＝1，2…，M}，Y＝{yj∈R3|j＝1，2，…，N}。
Preferably, matrix transforming the feature vector matrix comprises:
according to the obtained feature vector matrix
Figure BDA0002914764800000021
Calculating a rotation matrix R;
according to obtainingThe resulting rotation matrix R is y according to Tmean-R*xmeanCalculating a translation matrix T;
according to X according to the rotation matrix R and the translation matrix TinitComputing is performed on the source point cloud R X + T.
Preferably, intercepting the pixels of the unit length around the two feature point clouds and inputting the pixels into the CNN neural network for comparison, and determining the matching result of the two feature point clouds includes:
the calculated point cloud XinitThe target point cloud Y is used as the input of the CNN neural network;
respectively obtaining a feature map of a source point cloud X and a feature map of a target point cloud Y by using a CNN neural network;
and calculating the similarity between the feature maps of the source point cloud X and the target point cloud Y by using a cosine similarity algorithm.
Preferably, the calculating the similarity between the feature maps of the source point cloud X and the target point cloud Y by the cosine similarity algorithm includes:
and when the cosine similarity obtained by calculation is smaller than a preset threshold value, the cosine similarity is considered to be similar, otherwise, the cosine similarity is not similar.
On the other hand, the invention also provides a plane point cloud matching device based on principal component analysis, which comprises:
the principal component analysis module is used for extracting principal components of the two feature point clouds to obtain principal directions of the two feature point clouds;
the initial registration module is used for performing initial registration on the main directions of the two feature point clouds by using a Principal Component Analysis (PCA) method;
and the convolutional neural network module is used for intercepting pixels of unit length around the two feature point clouds and inputting the pixels into the CNN neural network for comparison so as to determine a matching result of the two feature point clouds.
Preferably, the device further comprises: a judgment module configured to
When the two feature point clouds are correctly matched, continuing to calculate the similarity of the two feature point clouds;
and outputting a dissimilar matching result when the two feature point clouds are not matched correctly.
Preferably, the initial registration module initially registers principal directions of the two feature point clouds using PCA principal component analysis, including:
carrying out characteristic value decomposition on the covariance information of the two characteristic point clouds to obtain characteristic vector matrixes corresponding to the two characteristic point clouds;
and performing matrix transformation on the feature vector matrix to register the main directions of the two feature point clouds.
The plane point cloud matching method and device based on principal component analysis can solve the problem that the quality precision of data acquisition is excessively depended on. The method and the device have the advantages that the registration processing capability of the point cloud data with large data volume is strong, and the algorithm time and space complexity is controllable. By utilizing the feature extraction of PCA and the pattern matching of CNN, the high-efficiency and high-resolution point cloud learning is realized.
The above and other objects, advantages and features of the present application will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
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Some specific embodiments of the present application will be described in detail hereinafter by way of illustration and not limitation with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
FIG. 1 is a schematic flow chart diagram of a principal component analysis-based planar point cloud matching method according to one embodiment of the present application;
FIG. 2 is a schematic structural diagram of a planar point cloud matching device based on principal component analysis according to an embodiment of the present application;
FIG. 3 is another schematic structural diagram of a planar point cloud matching device based on principal component analysis according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of a first computer-readable storage medium according to an embodiment of the present application;
fig. 5 is a schematic block diagram of a second computer-readable storage medium according to an embodiment of the present application.
Detailed Description
Fig. 1 is a plane point cloud matching method based on principal component analysis according to an embodiment of the present application, which may generally include steps S101 to S103:
s101, extracting main components of two feature point clouds to obtain main directions of the two feature point clouds;
s102, performing initial registration on the main directions of the two feature point clouds by using a Principal Component Analysis (PCA) method;
s103, intercepting the pixels of the unit length around the two feature point clouds, inputting the pixels into a CNN neural network for comparison, and determining the matching result of the two feature point clouds.
In the embodiment of the present invention, the method further includes:
when the two feature point clouds are correctly matched, continuing to calculate the similarity of the two feature point clouds;
when the two feature point clouds are not correctly matched, outputting a matching result: the two feature point clouds are dissimilar.
In the embodiment of the invention, when the two feature point clouds are correctly matched, the similarity can be continuously calculated. The mismatch can directly output the result: are not similar.
In the embodiment of the present invention, the step S102 of performing initial registration on the principal directions of the two feature point clouds by using a PCA principal component analysis method includes:
carrying out characteristic value decomposition on the covariance information of the two characteristic point clouds to obtain characteristic vector matrixes corresponding to the two characteristic point clouds;
and performing matrix transformation on the feature vector matrix to register the main directions of the two feature point clouds.
In the embodiment of the invention, step S102 uses PCA principal component analysis to perform initial registration on the principal directions of the two feature point clouds X and Y, so as to perform initial registration.
In the embodiment of the present invention, performing eigenvalue decomposition on the covariance information of the two feature point clouds to obtain the eigenvector matrix corresponding to the two feature point clouds includes:
respectively calculating the centroid coordinates of the source point cloud X and the target point cloud Y, and expressing the centroid coordinates as xmean and ymean;
respectively calculating covariance matrixes of a source point cloud X and a target point cloud Y, and expressing the covariance matrixes as Xcovar and Ycovar;
respectively carrying out eigenvalue decomposition on the two obtained covariance matrixes to obtain two corresponding eigenvector matrixes expressed as Xeigen and Yeigen,
wherein X ═ { X ═ Xi∈R3|i＝1，2，…，M}，Y＝{yj∈R3|j＝1，2，…，N}。
In the embodiment of the present invention, the matrix transformation according to the eigenvector matrix includes:
according to the obtained feature vector matrix
Figure BDA0002914764800000051
Calculating a rotation matrix R;
according to T = y based on the obtained rotation matrix Rmean-R*xmeanCalculating a translation matrix T;
according to X according to the rotation matrix R and the translation matrix TinitComputing is performed on the source point cloud R X + T.
In the embodiment of the present invention, intercepting the pixels of the unit length around the two feature point clouds and inputting the pixels into the CNN neural network for comparison, and determining the matching result of the two feature point clouds includes:
the calculated point cloud XinitThe target point cloud Y is used as the input of the CNN neural network;
respectively obtaining a feature map of a source point cloud X and a feature map of a target point cloud Y by using a CNN neural network;
and calculating the similarity between the feature maps of the source point cloud X and the target point cloud Y by using a cosine similarity algorithm.
In the examples of the present invention, XinitRepresenting the new point cloud obtained by transformation.
In the embodiment of the invention, the calculating the similarity between the feature maps of the source point cloud X and the target point cloud Y by using a cosine similarity algorithm comprises the following steps:
and when the cosine similarity obtained by calculation is smaller than a preset threshold value, the cosine similarity is considered to be similar, otherwise, the cosine similarity is not similar.
The preset threshold in the embodiment of the invention is artificially counted and determined through a large number of experimental comparisons to obtain the cosine similarity threshold with the best classification effect.
The process of the point cloud feature matching method facing the region in the embodiment of the invention is as follows:
the structure of the model utilized includes: PCA (principal component analysis), CNN (convolutional neural network)
Firstly, coarse matching is carried out: in the rough matching stage, principal components of the two characteristic point clouds are extracted by using a Principal Component Analysis (PCA) method, so that principal directions of the two point clouds are obtained. Assuming that the source point cloud X and the target point cloud Y are X ═ Xi∈Ｒ３｜i＝1，2，…，M}，Y＝{yj∈R31, 2, …, N, the flow of PCA-based initial registration is as follows:
a) respectively calculating the mass center coordinates of the two point cloud models: xmean and ymean;
b) respectively calculating covariance matrixes of two point cloud models: xcovar and Ycovar;
c) and respectively carrying out eigenvalue decomposition on the two covariance matrixes to obtain two eigenvector matrixes: xeigen and Yeigen;
d) calculating a rotation matrix:
Figure BDA0002914764800000061
e) calculating a translation matrix: y ═ Tmean-R*xmean；
f) Performing computations on the source point cloud: xniit＝R*X+T；
In the judgment of matching, the characteristic diagram extracted by the CNN neural network is used for judgment. And intercepting the pixels with unit length around the feature point, inputting the pixels into a CNN neural network for comparison, and judging whether the feature point is correctly matched or not by the CNN.
a) Taking the calculated point clouds Xnit and Y as the input of the neural network
b) Respectively obtaining feature maps of two pictures by using CNN neural network
c) Calculating similarity between graphs using cosine similarity algorithm
As shown in fig. 2, an embodiment of the present invention further provides a plane point cloud matching apparatus based on principal component analysis, including:
a principal component analysis module 100 configured to extract principal components of two feature point clouds to obtain principal directions of the two feature point clouds;
an initial registration module 200 configured to perform initial registration on principal directions of the two feature point clouds by using a Principal Component Analysis (PCA);
the convolutional neural network module 300 is configured to intercept pixels of unit length around the two feature point clouds and input the pixels into the CNN neural network for comparison, so as to determine a matching result of the two feature point clouds.
As shown in fig. 3, in the embodiment of the present invention, the apparatus further includes: a judging module 400 configured to
When the two feature point clouds are correctly matched, continuing to calculate the similarity of the two feature point clouds;
and outputting a dissimilar matching result when the two feature point clouds are not matched correctly.
In the embodiment of the present invention, the initially registering the two feature point clouds in the main direction by the initial registration module 200 using a PCA principal component analysis method includes:
carrying out characteristic value decomposition on the covariance information of the two characteristic point clouds to obtain characteristic vector matrixes corresponding to the two characteristic point clouds;
and performing matrix transformation on the feature vector matrix to register the main directions of the two feature point clouds.
Embodiments also provide a computing device, referring to fig. 4, comprising a memory 1120, a processor 1110 and a computer program stored in said memory 1120 and executable by said processor 1110, the computer program being stored in a space 1130 for program code in the memory 1120, the computer program, when executed by the processor 1110, implementing the method steps 1131 for performing any of the methods according to the invention.
The embodiment of the application also provides a computer readable storage medium. Referring to fig. 5, the computer readable storage medium comprises a storage unit for program code provided with a program 1131' for performing the steps of the method according to the invention, which program is executed by a processor.
The embodiment of the application also provides a computer program product containing instructions. Which, when run on a computer, causes the computer to carry out the steps of the method according to the invention.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed by a computer, cause the computer to perform, in whole or in part, the procedures or functions described in accordance with the embodiments of the application. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by a program, and the program may be stored in a computer-readable storage medium, where the storage medium is a non-transitory medium, such as a random access memory, a read only memory, a flash memory, a hard disk, a solid state disk, a magnetic tape (magnetic tape), a floppy disk (floppy disk), an optical disk (optical disk), and any combination thereof.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A plane point cloud matching method based on principal component analysis comprises the following steps:
extracting main components of two characteristic point clouds to obtain main directions of the two characteristic point clouds;
performing initial registration on the principal directions of the two feature point clouds by using a PCA principal component analysis method;
and intercepting pixels of unit length around the two feature point clouds, inputting the pixels into a CNN neural network for comparison, and determining a matching result of the two feature point clouds.
2. The method of claim 1, further comprising:
when the two feature point clouds are correctly matched, continuing to calculate the similarity of the two feature point clouds;
when the two feature point clouds are not correctly matched, outputting a matching result: the two feature point clouds are dissimilar.
3. The method of claim 1 or 2, wherein initially registering principal directions of the two feature point clouds using PCA principal component analysis comprises:
carrying out characteristic value decomposition on the covariance information of the two characteristic point clouds to obtain characteristic vector matrixes corresponding to the two characteristic point clouds;
and performing matrix transformation on the feature vector matrix to register the main directions of the two feature point clouds.
4. The method of claim 3, wherein performing eigenvalue decomposition on the covariance information of the two feature point clouds to obtain eigenvector matrices corresponding to the two feature point clouds comprises:
respectively calculating the centroid coordinates of the source point cloud X and the target point cloud Y, and expressing the centroid coordinates as xmean and ymean;
respectively calculating covariance matrixes of a source point cloud X and a target point cloud Y, and expressing the covariance matrixes as Xcovar and Ycovar;
respectively carrying out eigenvalue decomposition on the two obtained covariance matrixes to obtain two corresponding eigenvector matrixes expressed as Xeigen and Yeigen,
wherein X ═ { X ═ Xi∈R3|i＝1，2，…，M}，Y＝{yj∈R3|j＝1，2，…，N}。
5. The method of claim 4, wherein matrix transforming the eigenvector matrix comprises:
according to the obtained feature vector matrix
Figure FDA0002914764790000021
Calculating a rotation matrix R;
according to T ═ y, the rotation matrix R is obtainedmean-R*xmeanCalculating a translation matrix T;
according to X according to the rotation matrix R and the translation matrix TinitComputing is performed on the source point cloud R X + T.
6. The method of claim 5, wherein pixels of unit length around the two feature point clouds are intercepted and input into a CNN neural network for comparison, and determining the matching result of the two feature point clouds comprises:
the calculated point cloud XinitThe target point cloud Y is used as the input of the CNN neural network;
respectively obtaining a feature map of a source point cloud X and a feature map of a target point cloud Y by using a CNN neural network;
and calculating the similarity between the feature maps of the source point cloud X and the target point cloud Y by using a cosine similarity algorithm.
7. The method of claim 6, wherein calculating the similarity between the feature maps of the source point cloud X and the target point cloud Y using a cosine similarity algorithm comprises:
and when the cosine similarity obtained by calculation is smaller than a preset threshold value, the cosine similarity is considered to be similar, otherwise, the cosine similarity is not similar.
8. A planar point cloud matching device based on principal component analysis comprises:
the principal component analysis module is used for extracting principal components of the two feature point clouds to obtain principal directions of the two feature point clouds;
the initial registration module is used for performing initial registration on the main directions of the two feature point clouds by using a Principal Component Analysis (PCA) method;
and the convolutional neural network module is used for intercepting pixels of unit length around the two feature point clouds and inputting the pixels into the CNN neural network for comparison so as to determine a matching result of the two feature point clouds.
9. The apparatus of claim 8, further comprising: a judgment module configured to
When the two feature point clouds are correctly matched, continuing to calculate the similarity of the two feature point clouds;
and outputting a dissimilar matching result when the two feature point clouds are not matched correctly.
10. The apparatus of claim 8 or 9, wherein the initial registration module initially registers principal directions of the two feature point clouds using PCA principal component analysis comprises:
carrying out characteristic value decomposition on the covariance information of the two characteristic point clouds to obtain characteristic vector matrixes corresponding to the two characteristic point clouds;
and performing matrix transformation on the feature vector matrix to register the main directions of the two feature point clouds.
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CN114463396B (en) * 2022-01-07 2024-02-06 武汉大学 Point cloud registration method utilizing plane shape and topological graph voting
CN114723795A (en) * 2022-04-18 2022-07-08 长春工业大学 Bucket wheel machine unmanned operation positioning and mapping method based on improved nearest point registration

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