The visual SLAM algorithm based on the assumption of static environment will be affected by the motion feature points in dynamic environment leading to the degradation of system robustness and accuracy. To address this problem, a visual SLAM algorithm based on instance segmentation to remove dynamic feature points is proposed. The Yolact is used to detect potential motion objects, and the semantic information obtained is used to accurately reject dynamic feature points. In addition, considering the under-segmentation problem of the instance segmentation network, this algorithm combines the semantic information in historical key frames and depth maps to segment dynamic objects that are not detected by Yolact using a region growing algorithm, which further improves the robustness of the system. To ensure the real-time performance of the system, the instance segmentation network segments only the key frames, and improves the key frame selection strategy. The experimental results of the system based on TUM dataset show that compared with ORB-SLAM3, the ab-solute trajectory error and relative trajectory error of this algorithm in dynamic environment are reduced by 95.3% and 96.5%, respectively, the real-time performance of this algorithm is better compared with DynaSLAM and DS-SLAM.
The visual SLAM algorithm based on the assumption of static environment will be affected by the motion feature points in dynamic environment, resulting in the system robustness and accuracy degradation. To solve this problem, a visual SLAM algorithm for dynamic feature removal based on instance segmentation is proposed. Firstly, Yolact network is used to detect potential moving objects, and the obtained semantic information is combined with epipolar geometric constraints to accurately eliminate dynamic feature points. Considering the failure of example segmentation network, this algorithm combines the semantic information in the historical key frame with the depth map and uses the region growth algorithm to segment the dynamic objects that are not detected by Yolact, which further improves the robustness of the system. The experimental results show that compared with ORB-SLAM3, the absolute trajectory error and relative trajectory error of the algorithm in the dynamic environment are significantly reduced.
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