智能科学与技术学报 (Dec 2023)
Point cloud registration method based on principal component analysis and feature map matching
Abstract
Due to varying degrees of overlap in point cloud models, point cloud registration is prone to problems, such as feature matching errors and high difficulty in registration. Therefore, a point cloud registration method based on principal component analysis and feature map matching is proposed. Before registration, the principal component analysis method with spindle correction was used to adjust the initial pose, then the K-dimensional tree was established to search the overlapping area. Secondly, the fast point feature histograms features of the sampling points were calculated according to the overlapping area of the two-point cloud, and the point cloud feature graph matching and trimmed iterative closest point (TrICP) fine registration were performed. Registration experiments were carried out according to the existing datasets and the actual scanning model. The experimental results show that the method has good stability and higher accuracy, and the accuracy can be improved by more than 25% compared with other algorithms.
