Journal
APPLIED SCIENCES-BASEL
Volume 12, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/app122211548
Keywords
dynamic SLAM; YOLOv5 network; deep learning
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This paper proposes a new mapping method based on the ORB-SLAM2 algorithm combined with the YOLOv5 network, which addresses the common issues when building maps in dynamic environments. The proposed algorithm outperforms comparable algorithms in terms of accuracy and real-time performance.
When building a map of a dynamic environment, simultaneous localization and mapping systems have problems such as poor robustness and inaccurate pose estimation. This paper proposes a new mapping method based on the ORB-SLAM2 algorithm combined with the YOLOv5 network. First, the YOLOv5 network of the tracing thread is used to detect dynamic objects of each frame, and to get keyframes with detection of dynamic information. Second, the dynamic objects of each image frame are detected using the YOLOv5 network, and the detected dynamic points are rejected. Finally, the global map is constructed using the keyframes after eliminating the highly dynamic objects. The test results using the TUM dataset show that when the map is constructed in a dynamic environment, compared with the ORB-SLAM2 algorithm, the absolute trajectory error of our algorithm is reduced by 97.8%, and the relative positional error is reduced by 59.7%. The average time consumed to track each image frame is improved by 94.7% compared to DynaSLAM. In terms of algorithmic real-time performance, this paper's algorithm is significantly better than the comparable dynamic SLAM map-building algorithm DynaSLAM.
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