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Point cloud matching algorithm based on EPFH features

A point cloud matching and feature point technology, applied in the field of point cloud data, can solve the problems that the features are easily affected by noise, take a long time, and have low matching accuracy.

Pending Publication Date: 2019-09-06
XIAN UNIV OF FINANCE & ECONOMICS
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Problems solved by technology

[0005] However, the above methods have problems such as low matching accuracy, long time consumption, and features are easily affected by noise.

Method used

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  • Point cloud matching algorithm based on EPFH features
  • Point cloud matching algorithm based on EPFH features
  • Point cloud matching algorithm based on EPFH features

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specific Embodiment 1

[0075] see figure 1 Shown, the present invention is a kind of point cloud matching algorithm based on EPFH feature, comprises the following steps:

[0076] S0: establish a local coordinate system;

[0077] S1: component EPFH feature descriptor;

[0078] S2: Using principal component analysis to reduce the dimensionality of EPFH feature descriptors;

[0079] S3: Use the sampling consistency method to achieve rough matching between point clouds;

[0080] Among them, the specific process of rough matching is as follows:

[0081] S30: Select n sample feature points from the feature point set of the point cloud P to be matched;

[0082] Wherein, the distance between the sample feature points is not less than the preset minimum distance threshold d;

[0083] S31: Find one or more similar points with similar EPFH characteristics to the sampling points in the point cloud P to be matched in the feature point set of the target point cloud Q, and randomly select one from the similar...

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Abstract

The invention discloses a point cloud matching algorithm based on EPFH features, and relates to the technical field of point cloud data. The method comprises the steps of establishing a local coordinate system; a component EPFH feature descriptor; reducing the dimension of the EPFH feature descriptor by adopting a principal component analysis method; adopting a sampling consistency method to realize coarse matching between the point clouds; using a k-d tree search corresponding point pair based ICP algorithm to realize fine matching of the two point clouds. According to the method, dimensionality reduction is carried out on an EPFH feature descriptor through a component local coordinate system by adopting a principal component analysis method; coarse matching between point clouds is realized by adopting a sampling consistency method, and then the k-d tree search corresponding point pair based ICP algorithm is used to to realize fine matching of the two point clouds and realize fine matching of the two point clouds.

Description

technical field [0001] The invention belongs to the technical field of point cloud data, in particular to a point cloud matching algorithm based on EPFH features. Background technique [0002] With the rapid development of 3D laser scanning equipment, the processing of point cloud data has received extensive attention. In particular, since the scanning device is limited by the measurement environment and the device itself, it is impossible to complete the scanning of the entire entity at one time. Therefore, how to align the point cloud data scanned under different viewing angles has become a hot research topic of many scholars this year. Point cloud matching is the study of this problem. By finding the optimal rigid body transformation to align the point clouds under different viewing angles, the matching accuracy plays a vital role in the subsequent processing of the 3D model. [0003] The most classic method to solve this problem is the Iterative Closest Point (ICP) alg...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/33G06K9/46G06K9/62
CPCG06T7/33G06T2207/10028G06V10/50G06F18/2135
Inventor 汤慧钟飞王帑
Owner XIAN UNIV OF FINANCE & ECONOMICS
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