A Local-to-Global Point Cloud Registration Algorithm Based on Joint Matching of Multi-keypoints

Point cloud registration is a fundamental challenge in the field of 3D vision, particularly when dealing with scans that have limited overlapping features. In this paper, a local-to-global point cloud registration algorithm based on multi-keypoints joint matching is proposed. The algorithm incorporates neighborhood keypoints distance constraint and multi-keypoints joint matching scheme to establish robust and weighted correspondences. Furthermore, a fast segmentation and registration method is designed for overlapping regions, utilizing the weighted correspondences. The proposal algorithm achieves the expected results and is superior to mainstream traditional registration algorithms in terms of efficiency and accuracy.