DPL-SLAM: Enhancing Dynamic Point-Line SLAM Through Dense Semantic Methods

The traditional visual Simultaneous Localization and Mapping (SLAM) systems rely on the static-world assumption and cannot handle dynamic objects. This paper presents a novel SLAM system, Semantic Point and Line Features SLAM (DPL-SLAM), that can handle dynamic environments and can be used for real-time operation. To handle dynamic objects, we apply object detection to identify 80 categories within the scene and implement unique handling of features both within and outside the detected bounding boxes using Lucas-Kanade (LK) optical flow and epipolar constraint. Within bounding boxes, we propose an efficient local elimination algorithm to address features that violate the epipolar constraint. We designate nearby and intra-box regions that deviate from the constraint as potential dynamic areas, and conditionally eliminate features within these areas to varying extents, thus minimizing incorrect elimination of stable data associations. Outside the bounding boxes, non-compliant features are regarded as outliers and directly eliminated, making the system robust to unknown objects. We have evaluated DPL-SLAM on the TUM RGB-D and KITTI STEREO datasets and compared it with state-of-the-art SLAM systems. The results show that DPL-SLAM outperforms most SLAM systems in various dynamic scenarios and exhibits excellent robustness and realtime performance, thus effectively handling dynamic noise interference under indoor RGB-D and outdoor stereo modes. Finally, we conduct experiments in a real-world environment to verify the algorithm's effectiveness.