中国光学（中英文）2025，Vol.18Issue(5)：1076-1085,10.DOI:10.37188/CO.2025-0073
动态扫描场景下GM-APD激光雷达点云高精度配准方法研究
High-precision registration methods for GM-APD LiDAR point clouds in dynamic scanning scenarios
摘要
Abstract
Aiming at the issues of low overlapping rates of adjacent-frame point clouds and the tendency to forcibly register non-matching point pairs in Geiger-mode avalanche photodiode(GM-APD)LiDAR when used in dynamic scanning conditions,an enhanced iterative closest point(ICP)algorithm is proposed based on a bidirectional matching scheme and multi-resolution neighborhood expansion to improve registration ac-curacy and robustness.First,a K-D tree-based bidirectional search identifies overlapping regions between consecutive frames,enabling accurate initial alignment.Then,a high-resolution neighborhood expansion ap-proach,weighted by local curvature similarity,is applied to refine the transformation matrix and suppress mismatched correspondences.Finally,a cascaded compensation mechanism ensures global consistency across frames.Experiment results demonstrate that our method achieves average distance errors of 0.21 m(2 km scene)and 0.10 m(400 m scene),effectively improving registration precision in dynamic scenarios and offering valuable support for 3D reconstruction.关键词
GM-APD/点云配准/位姿校正/双向匹配/多分辨率邻域扩展Key words
GM-APD/point cloud registration/pose correction/bidirectional matching/multi-resolution neighborhood expansion分类
信息技术与安全科学引用本文复制引用
钟国舜,刘秋佐,李萌,彭涛,孙剑峰,刘建伟..动态扫描场景下GM-APD激光雷达点云高精度配准方法研究[J].中国光学（中英文）,2025,18(5):1076-1085,10.基金项目
国家自然科学基金(No.62105240,No.62075159) （No.62105240,No.62075159）
国家重点研发计划(No.2019YFB2203002)Supported by National Natural Science Foundation(No.62105240,No.62075159) （No.2019YFB2203002）
National Key R & D Plan(No.2019YFB2203002) （No.2019YFB2203002）
