Impact Factor:5.3
DOI number:10.1007/s10489-023-04531-6
Journal:APPLIED INTELLIGENCE
Key Words:Semantic SLAM; Dynamic scenes; Epipolar constraints; Point cloud map
Abstract: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.
Indexed by:Journal paper
Volume:53
Issue:16
Page Number:19418-19432
Translation or Not:no
Date of Publication:2023-03-27
Included Journals:SCI