Global Registration

Global registration aims to accurately determine the relative pose (position and orientation) between two 3D point clouds, a crucial task in various applications like robotics and 3D mapping. Current research focuses on improving robustness to outliers and computational efficiency, employing techniques like graph-based maximal clique search (e.g., using branch-and-bound or improved pruning algorithms), iterative refinement strategies leveraging dynamic point selection, and incorporating semantic information (e.g., ground segmentation). These advancements enable more accurate and faster global registration, particularly in challenging scenarios with sparse data or significant pose discrepancies, leading to improvements in applications such as LiDAR SLAM and 3D scene reconstruction.