Abstract
The assumption of static scene is typical in SLAM algorithms, which limits the use of visual SLAM systems in real-world dynamic environments. Dynamic elimination, detecting or segmenting the static and dynamic region in the image and regarding the features in dynamic parts as outliers, is proved to an effective solution to solve the dynamic SLAM problem. However, traditional dynamic elimination methods processing each frame are very time consuming. In this paper, dynamic elimination is implemented only on keyframes utilizing YOLO as the fast dynamic detection network. This keyframe-based improvement ensures localization accuracy by ensuring map accuracy, and at the same time increases the speed of the SLAM system with dynamic elimination greatly. Experiments are conducted both in real-world environment and on the public TUM datasets. The results demonstrate the effectiveness as well as efficiency of our method.
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Jin, G., Zhong, X., Fang, S., Deng, X., Li, J. (2019). Keyframe-Based Dynamic Elimination SLAM System Using YOLO Detection. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11743. Springer, Cham. https://doi.org/10.1007/978-3-030-27538-9_60
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DOI: https://doi.org/10.1007/978-3-030-27538-9_60
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