| Citation: | ZHONG Guo-shun, LIU Qiu-zuo, LI Meng, PENG Tao, SUN Jian-feng, LIU Jian-wei. Research on high-precision registration methods for GM-APD LiDAR point clouds in dynamic scanning scenarios[J]. Chinese Optics. doi: 10.37188/CO.2025-0073
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This paper addresses the challenges of low overlap and mismatched point pairs in Geiger-mode avalanche photodiode (GM-APD) LiDAR point clouds under dynamic scanning conditions. To improve registration accuracy and robustness, an enhanced Iterative Closest Point (ICP) algorithm is proposed, integrating a bidirectional matching scheme and multi-resolution neighborhood expansion. First, a K-D tree-based bidirectional search identifies overlapping regions between consecutive frames, enabling accurate initial alignment. Then, a high-resolution neighborhood expansion approach, 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. Experiments 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.
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