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Dynamic visual simultaneous localization and mapping based on semantic segmentation module
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DOI：10.1007/s10489-023-04531-6.png)
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摘要
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Simultaneous localization and mapping (SLAM) is a key technique for mobile robotics. Moving objects can vastly impair the performance of a visual SLAM system. To deal with the problem, a new semantic visual SLAM system for indoor environments is proposed. Our system adds a semantic segmentation network and geometric model to detect and remove dynamic feature points on moving objects. Moreover, a 3D point cloud map with semantic information is created using semantic labels and depth images. We evaluate our method on the TUM RGB-D dataset and real-world environments. The evaluation metrics used are absolute trajectory error and relative position error. Experimental results show our method improves the accuracy in dynamic scenes compared to ORB-SLAM3 and other advanced methods.
Keyword:
Semantic SLAM
Dynamic scenes
Epipolar constraints
Point cloud map
期刊
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3.5
论文数:
7.6K
被引数:
1.7W
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