DFPC-SLAM: A Dynamic Feature Point Constraints-Based SLAM Using Stereo Vision for Dynamic Environment

Abstract

Visual SLAM methods usually presuppose that the scene is static, so the SLAM algorithm for mobile robots in dynamic scenes often results in a significant decrease in accuracy due to the influence of dynamic objects. In this paper, feature points are divided into dynamic and static from semantic information and multi-view geometry information, and then static region feature points are added to the pose-optimization, and static scene maps are established for dynamic scenes. Finally, experiments are conducted in dynamic scenes using the KITTI dataset, and the results show that the proposed algorithm has higher accuracy in highly dynamic scenes compared to the visual SLAM baseline.