3D point cloud matching based on principal component analysis and iterative closest point algorithm

Abstract

Point cloud matching is one of the key technologies of optical three-dimensional contour measurement. Most of the point cloud matching without landmark used the iterative closest point algorithm. In order to improve the performance of the iterative closest point algorithm, the two-step iterative closest point algorithm was proposed. The improved algorithm is divided into a rough matching step and accurate matching step. Rough matching used the principal component analysis algorithm, while the fine matching used the improved iterative closest point algorithm. Compared with the classic iterative closest point algorithm, the improved algorithm can match the partial coincident point cloud. At the same time, the experiment can validate the effectiveness of the proposed algorithm.