A LiDAR SLAM System With Semantic-Enhanced Odometry and Loop-Closure

LiDAR SLAM technology plays a critical role in applications such as 3D mapping and autonomous navigation. However, most existing SLAM systems are built on the assumption of a static environment, which is frequently violated in real-world scenarios. This results in significant map noise and incorrect data associations caused by dynamic elements. In addition, while loop closure modules help mitigate cumulative drift in LiDAR odometry, their performance often degrades severely in the presence of viewpoint changes and geometrically degenerate environments. To address these challenges, a semantic-aided LiDAR odometry method is introduced, which first leverages semantic and geometric information to extract different types of feature points, and then estimates odometry under semantic consistency constraints. Furthermore, a semantic-enhanced loop closure network is designed, which incorporates a Semantic-Spatial Fusion (SSF) module to integrate loop closure and semantic features, and a Correspondence Confidence Evaluation (CCE) module to filter out unreliable point correspondences for registration. These two components are then integrated into a complete semantic SLAM system, enabling the construction of globally consistent semantic maps. Experiments on the SemanticKITTI dataset and a customized campus scenario validate the robustness of the proposed loop closure network in both loop closure detection and global registration. When integrated with the semantic-enhanced odometry and loop closure, the resulting system achieves higher mapping accuracy than several existing SLAM methods and produces globally consistent semantic maps.