A robust correspondence-based registration method for large-scale outdoor point cloud

Abstract

Large-scale outdoor point cloud registration is essential for the 3D reconstruction of outdoor scenes. Its central objective is to achieve accurate point cloud registration by determining accurate spatial transformation parameters. While feature-based methods eliminate the need for initial position estimation, they encounter challenges in handling high outlier rates. Therefore, a method capable of effectively managing outliers is crucial for enhancing the efficiency and accuracy of large-scale outdoor point cloud registration. This paper introduces the maximal clique with adaptive voting (MCAV) method, which leverages graph-based inlier compatibility to optimize potential matches. MCAV employs adaptive parameter voting (APV) to enhance computational efficiency, demonstrating significant speedup characteristics in datasets with a significant number of inliers. To further reduce outliers in potential matches, we integrate Black-Rangarajan Duality (BRD) and graduated non-convexity (GNC) into the truncated least squares (TLS) framework (BG-TLS). Accordingly, we propose the efficient BG-TLS (EBG-TLS) method for computing the registration model. Comparative analyses with traditional and deep learning-based methods across various real-world environments demonstrate that the proposed method outperforms existing algorithms in terms of rotation error, translation error, and efficiency, particularly in complex, high-noise settings. This method finds broad applications in geospatial mapping and surveying, autonomous navigation, and environmental monitoring.

Keywords

• Point cloud registration
• maximal cliques
• Black-Rangarajan duality
• graduated non-convexity
• truncated least squares