A point cloud registration method based on multiple-local-feature matching

Abstract

A dual-view point cloud registration method has been proposed to enhance the accuracy of point-cloud registration by utilizing multiple local feature descriptors. The method focuses on studying the eigenvalues of the point cloud set at various scales and the impact of descriptors that consist of corresponding curvature radii in reducing misregistration in fast point feature histogram (FPFH). The optimal scale size and number were determined through experimental analysis. SIFT-3D key points of point clouds were extracted to calculate their FPFH and complete initial correspondence matching. Next, a multi-neighborhood local feature descriptor was designed to filter correspondence. A large number of incorrect corresponding point pairs were removed after the initial FPFH matching, and the remaining correct point pairs were used for rough registration. Finally, the improved ICP algorithm was used to reduce registration errors and achieve more accurate dual-view registration. According to the experiments of the public point cloud data set, the dual-view point cloud registration algorithm implemented by the method has good accuracy, high robustness, and more reliable point cloud registration results, which provides a theoretical basis for 3D point-cloud reconstruction.