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For Simultaneous Localization and Mapping (SLAM) systems in static environments, the algorithms that have matured so far have obtained more accurate trajectories. Still, the optimization results of the back end in dynamic environments could be better due to the influence of various dynamic targets. Existing algorithms could perform better in dynamic environments with high illumination variation, low texture, and many dynamic targets. This paper uses a YOLOv5-based target detection module to screen potential dynamic targets in the front end and verify and reject dynamic feature points by subsequent polar line constraints. Experimental results show that such an approach can improve the system’s stability. The experimental results also show that the results are improved compared to ORB-SLAM2 for both indoor and outdoor dynamic environment datasets.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xiang Zhang andTao Jiang
"Improved SLAM algorithm based on target detection in dynamic environments", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129703C (21 December 2023); https://doi.org/10.1117/12.3012518
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Xiang Zhang, Tao Jiang, "Improved SLAM algorithm based on target detection in dynamic environments," Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129703C (21 December 2023); https://doi.org/10.1117/12.3012518