LAUSR

LiDAR simultaneous localization and mapping (SLAM) technology plays a critical role in applications such as 3-D mapping and autonomous navigation. However, most existing SLAM systems are built on the assumption of a static environment, which is frequently violated in realworld 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 module (SSFM) to integrate loop-closure and semantic features, and a correspondence confidence evaluation module (CCEM) 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.