SLOOP: Aligned Coordinate System-aided LiDAR LOOP Closure Detection based on Semantic Node Graph Matching

Abstract

Loop closure detection and pose estimation play a significant role in correcting odometry trajectories and generating globally consistent point cloud maps. Geometric feature descriptor methods neglect object-level spatial topology features, resulting in inadequate performance in loop closure detection. Semantic graph-based loop closing methods improve upon this, however, they still follow the paradigm of "first generating descriptors, then comparing similarity, and finally achieving alignment (6D pose)". Specifically, they compare two semantic graphs that are not spatially aligned, which makes direct node correspondences impossible and necessitates extensive descriptor extraction and comparison. This decouples similarity comparison from 6D pose estimation, resulting in a cumbersome process that limits practicality and scalability. This paper proposes SLOOP, a novel descriptor-free semantic graph matching method that "aligns two graphs first, followed by efficient similarity comparison". Specifically, we first design a dedicated neighborhood semantic feature module to extract high-quality matched node pairs. Next, we seek the aligned coordinate systems for candidate loops based on the robust ground normal vectors and two suitable node pairs examined by the two-stage global geometric consistency metrics. Finally, the aligned coordinate systems enable efficient extraction and comparison of node spatial distributions. We conducted extensive outdoor loop detection experiments and compared with various loop closure detection approaches, demonstrating the improved performance of SLOOP in loop closure detection and its practicality. The code and related materials are available at https://github.com/bit-tyj/sloop_c.