Abstract
To enhance the localization accuracy and robustness of the visual SLAM algorithm in dynamic environments, this paper proposes a methodology that relies on target detection and direct geometric constraints. The algorithm first obtains static feature points and possible dynamic feature points of the current frame using a YOLOV7 target detection network. It then judges the real dynamic target using the geometric change relationship between the edges connecting the feature points of two adjacent frames. Based on the motion information of the dynamic target in past frames, the potential dynamic targets of the current frame are again examined, all feature points in the dynamic target frame are removed. Comparative experiments on the TUM dataset show that the proposed algorithm reduces the absolute trajectory error by an average of 94.69% compared to ORB-SLAM2. It outperforms mainstream dynamic vision SLAM schemes such as Dyna-SLAM and DS_SLAM in terms of localization accuracy.
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This research was supported by the Project of China West Normal University under Grant 17YC046.
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Lin, J., Feng, Z., Tang, J. (2023). Visual SLAM Algorithm Based on Target Detection and Direct Geometric Constraints in Dynamic Environments. In: Yongtian, W., Lifang, W. (eds) Image and Graphics Technologies and Applications. IGTA 2023. Communications in Computer and Information Science, vol 1910. Springer, Singapore. https://doi.org/10.1007/978-981-99-7549-5_7
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DOI: https://doi.org/10.1007/978-981-99-7549-5_7
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