PLM-SLAM: Enhanced Visual SLAM for Mobile Robots in Indoor Dynamic Scenes Leveraging Point-Line Features and Manhattan World Model

Abstract

This paper proposes an enhanced visual simultaneous localization and mapping (vSLAM) algorithm tailored for mobile robots operating in indoor dynamic scenes. By incorporating point-line features and leveraging the Manhattan world model, the proposed PLM-SLAM framework significantly improves localization accuracy and map consistency. This algorithm optimizes the line features detected by the Line Segment Detector (LSD) through merging and pruning strategies, ensuring real-time performance. Subsequently, dynamic point-line features are rejected based on Lucas–Kanade (LK) optical flow, geometric constraints, and depth information, minimizing the impact of dynamic objects. The Manhattan world model is then utilized to reduce rotational estimation errors and optimize pose estimation. High-precision line feature matching and loop closure detection mechanisms further enhance the robustness and accuracy of the system. Experimental results demonstrate the superior performance of PLM-SLAM, particularly in high-dynamic indoor environments, outperforming existing state-of-the-art methods.