KPS: The Key Plane Structure for loop closure detection in indoor LiDAR SLAM

Abstract

Simultaneous Localization and Mapping (SLAM) is a core technology for indoor mobile LiDAR mapping systems. In GNSS-denied environments, localization accuracy is often degraded by cumulative drift. Loop closure detection remains challenging under large viewpoint variations and limited scan overlap. To address this challenge, this paper proposes a loop closure detection method based on Key Plane Structures (KPS). Unlike point-based methods that are sensitive to sensor scanning angles, the proposed method exploits the geometric stability of indoor plane structures to construct invariant global descriptors. This representation improves the robustness of loop closure detection across different viewpoints and provides more reliable loop closure constraints for pose graph optimization. Extensive experiments on real-world datasets validate the effectiveness of the method. In scenarios with large viewpoint variations, the method reduces the absolute trajectory translation error to 0.248 m, significantly outperforming Scan Context. In scenarios with sufficient scan overlap, it achieves centimeter-level positioning accuracy. Overall, the proposed method reduces trajectory drift by approximately 68%–97% compared to the pure odometry baseline, demonstrating stable performance in indoor LiDAR SLAM.