Robust Low-Overlap Point Cloud Registration via Edge-Guided Features and Quaternion Averaging

Robust point cloud registration under low-overlap conditions remains a significant challenge in 3D computer vision and perception. To address this issue, we propose a novel registration framework that integrates edge-guided feature extraction, FPFH-based correspondence estimation, and quaternion averaging. The proposed method begins by detecting edge features through a normal-extrema-based strategy, which identifies geometrically salient points to enhance structural consistency in sparse overlapping regions. Next, FPFH descriptors are employed to establish point correspondences, followed by quaternion averaging to obtain a globally consistent initial alignment. Finally, a point-to-plane ICP refinement step is applied to improve the registration precision. Comprehensive experiments are conducted on three benchmark datasets—Stanford Bunny, Dragon, and Happy Buddha—to evaluate the performance of the proposed method. Compared with classical ICP and RANSAC-ICP algorithms, our method achieves significantly improved registration accuracy under low-overlap conditions, with the highest improvement reaching 75.7%. The results demonstrate the effectiveness and robustness of the proposed framework in challenging partial overlap scenarios.