The Semantic Point & Line SLAM for Indoor Dynamic Environment

Simultaneous Localization and Mapping (SLAM) technology has important implications for the autonomous navigation of intelligent mobile robots. In the past few years, many excellent visual SLAM systems were born, and most of them can do a good job in a static environment. However, in dynamic scenes,...

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Bibliographic Details
Main Authors: Zhenghang, Guo, Xinchun, Ji, Dongyan, Wei, Chao, Xie, Jingyu, Zhang
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Dynamic
Heuristic algorithms
Indoor navigation
Point-Line Features
Pose estimation
Semantic SLAM
Semantics
Simultaneous localization and mapping
Visualization
Online Access:Request full text
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Summary:Simultaneous Localization and Mapping (SLAM) technology has important implications for the autonomous navigation of intelligent mobile robots. In the past few years, many excellent visual SLAM systems were born, and most of them can do a good job in a static environment. However, in dynamic scenes, unreliable feature points in the scene will lead to the decline of system positioning accuracy and even cause system failure. Traditional methods often use the removal of dynamic points to solve dynamic scene problems, but in some environments, the reduction of feature points will also affect the positioning accuracy. Therefore, based on the ORB-SLAM2 visual SLAM system, this paper proposes a semantic point and line SLAM system for an indoor dynamic environment. The improved SLAM system has good performance in an indoor dynamic environment. Finally, we evaluate our algorithm on the TUM RGB-D dynamic dataset. The results show that the absolute Trajectory Accuracy of SPL-SLAM is significantly improved compared with the original ORB-SLAM2.
ISSN:2471-917X
DOI:10.1109/IPIN54987.2022.9918122