Dynamic visual SLAM based on improved ORB-SLAM3 tracking thread

In dynamic scenes, it is necessary to effectively identify and eliminate dynamic feature points for SLAM systems to maintain localization accuracy and accurate map construction. At present, obtaining richer semantic information for dynamic object detection based on deep learning algorithms has shown its great advantages. Traditional visual SLAM is robust in static scenes, but in challenging environments, how to improve the real-time performance, positioning accuracy and adaptability of SLAM remains to be solved. In this paper, based on the ORB-SLAM3 framework, YOLOv5 object detection and semantic segmentation module are fused to extract the key point information of obvious and potential dynamic objects. Secondly, combined with the optical flow method, a two-stage tracking strategy is proposed to track dynamic feature points and static feature points. The depth key point information and geometric constraint method are combined to predict the potential dynamic object and eliminate the dynamic feature points. The deep learning network is used to improve the image feature extraction in the dynamic environment, and the stable feature point feature extraction and dynamic feature point elimination are realized, so as to improve the robustness of the SLAM system. Finally, experimental evaluation on public datasets proves that the proposed method is superior to the ORB-SLAM3 algorithm in terms of accuracy and robustness in dynamic scenes.