Reliable LiDAR Loop Detection through Structural Descriptors and Semantic Graph Matching

Abstract

Outdoor loop closure detection is essential for mitigating accumulated drift in SLAM and generating a global consistent map. Semantic graph matching methods utilize object-level topology for distinctive scene representation but rely on environments with rich and distinguishable objects. Moreover, accurately matching nodes remains difficult due to ambiguities among same-class semantic nodes. These challenges limit their effectiveness in varied road environments, highlighting the need for representations that are both robust and adaptable. To address this, we introduce SD-SGM, a novel loop closure detection framework combining the powerful context-adaptation capabilities of structural descriptors with the high-level semantic reasoning abilities of semantic graphs. Initially, we extract semantic graphs alongside global structural descriptors from point clouds. Distinctive local graph features are then used to generate candidate node pairs, and the maximal clique algorithm identifies correspondences that are globally consistent. The similarity scores of both methods are then evaluated and a cross-validation mechanism assesses their reliability and adaptively weights them. Extensive loop closure detection experiments on various datasets demonstrate that SD-SGM achieves state-of-the-art (SOTA) performance compared to strong baselines. Additionally, we verify its effectiveness in improving SLAM trajectory accuracy. We provide the code at: https://github.com/BIT-TYJ/SD-SGM.