Point Cloud Mapping and Loop Closure Detection Using Superpoint Semantic Graph for Autonomous Driving

Accurate loop closure detection and pose estimation remain critical challenges for autonomous vehicles operating in dynamic urban environments, where perceptual aliasing, occlusions, and changing scenes often degrade localization performance. To this end, we present a novel hierarchical framework that leverages superpoint graphs to achieve robust place recognition and precise pose estimation. Our approach begins by constructing a topologically meaningful superpoint graph, where nodes represent stable environmental features and edges encode their spatial relationships. For loop closure detection, we introduce semantic-enhanced ring descriptors that combine geometric structure with semantic information, enabling reliable place recognition despite viewpoint changes or temporary occlusions. The system employs a two-stage verification process: initial candidate selection through descriptor matching, followed by geometric verification using superpoint centroids with RANSAC-based outlier rejection. The pose estimation pipeline employs a hierarchical refinement strategy, starting with superpoint centroid alignment, followed by dense ICP and sparse point-to-plane ICP, all integrated into a global pose graph optimization framework. Our overlap-based loop closure detection demonstrates superior performance across KITTI, Apollo, and Ford Campus datasets, achieving state-of-the-art SOTA results on AUC, F1MAX, and recall rate. Furthermore, our pose estimation method exhibits consistently outstanding performance in both accuracy and robustness.