Dynamic visual simultaneous localization and mapping based on semantic segmentation module
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影响因子：5.3
DOI码：10.1007/s10489-023-04531-6
发表刊物：APPLIED INTELLIGENCE
关键字：Semantic SLAM; Dynamic scenes; Epipolar constraints; Point cloud map
摘要：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.
论文类型：期刊论文
卷号：53
期号：16
页面范围：19418-19432
是否译文：否
发表时间：2023-03-27
收录刊物：SCI