基于动态区域剔除的 RGB-D 视觉 SLAM 算法.

Aiming at the problem that the visual SLAM algorithm in dynamic scenes is susceptible to motion feature points, resulting in low accuracy and poor robustness of pose estimation, this paper proposed a RGB-D visual SLAM algorithm based on dynamic region culling. Firstly, identify the feature points belonging to the moving objects with the help of semantic information, and detected whether the feature points remain stationary at this point used multi-view geometry with the help of the depth information of the camera. Use feature points extracted from static objects and static feature points derived from movable objects to fine-tune the camera pose estimation to achieve accurate and robust operation of the system in dynamic scenes. Finally, it used the dynamic indoor scenes in the TUM dataset for experimental verification. Experiments show that the proposed algorithm can effectively improve the accuracy of the camera′s pose estimation in the indoor dynamic environment, realize the map update in the dynamic environment, and improve the accuracy of the environment construction while enhancing the robustness of the system. [ABSTRACT FROM AUTHOR]