Research on Point Cloud Registration Based on Key Points and Matching Point Pairs Algorithm

Point cloud registration is a prominent topic in computer vision research, with applications including target identification, 3D reconstruction, SLAM, and others. The capture of key points and matching point pairs has evolved into a critical technology for point cloud registration. In this paper, we propose an approach that extracts key points using the Intrinsic Shape Signature (ISS) algorithm. Additionally, we combine the Fast Point Feature Histograms (FPFH) descriptor with curvature to obtain matching point pairs, thereby establishing a solid foundation for point cloud registration. The experiment shows that, compared to the 3D-SIFT technique, the key points derived by the ISS algorithm have a more uniform distribution. Moreover, our proposed combination of the FPFH descriptor with curvature extracts matching point pairs more effectively than using the FPFH descriptor alone.