基于主成分特征向量的点云配准方法

doi:10.3969/j.issn.0255-8297.2024.06.006

Abstract

Abstract: To address the issues of low accuracy and long time consumption of the existing point cloud registration algorithms for cluttered point clouds, a point cloud registration method based on principal component eigenvectors is proposed. Firstly, feature point set is extracted by describing the curvature change of the point cloud, and the center of gravity method is applied to align the center of gravity of the reference point cloud with that of the feature point set, achieving an initial rough registration. Then, during the iterative closest point (ICP) algorithm, principal component analysis (PCA) is used to select the first three principal component feature vectors and perform corresponding matching through rigid body transformation. Lastly, the Euclidean distance is used to find the nearest points for fine registration. The proposed method was validated using both public point cloud and cultural relic point cloud. Experimental results show that the registration accuracy of the proposed method is improved by approximately 12% on average, while the registration time is reduced by about 10% on average. These results indicate that the proposed method based on principal component eigenvectors is an effective approach for point cloud registration.

Key words: point cloud registration, curvature, iteration closest point, principal component analysis, eigenvector

CLC Number:

TP391

ZHAO Fuqun, HUANG He, GENG Guohua. Point Cloud Registration Method Based on Principal Component Eigenvectors[J]. Journal of Applied Sciences, 2024, 42(6): 962-976.

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Point Cloud Registration Method Based on Principal Component Eigenvectors

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