A Dynamic Scene Vision SLAM Method Incorporating Object Detection and Object Characterization

Hongliang Guan, Chengyuan Qian, Tingsong Wu, Xiaoming Hu, Fuzhou Duan () and Xinyi Ye
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Hongliang Guan: Engineering Research Center of Spatial Information Technology, MOE, Capital Normal University, 105 West Third Ring North Road, Haidian District, Beijing 100048, China
Chengyuan Qian: Engineering Research Center of Spatial Information Technology, MOE, Capital Normal University, 105 West Third Ring North Road, Haidian District, Beijing 100048, China
Tingsong Wu: Engineering Research Center of Spatial Information Technology, MOE, Capital Normal University, 105 West Third Ring North Road, Haidian District, Beijing 100048, China
Xiaoming Hu: Beijing Jumper Science Co., Ltd., Beijing 100083, China
Fuzhou Duan: Engineering Research Center of Spatial Information Technology, MOE, Capital Normal University, 105 West Third Ring North Road, Haidian District, Beijing 100048, China
Xinyi Ye: Engineering Research Center of Spatial Information Technology, MOE, Capital Normal University, 105 West Third Ring North Road, Haidian District, Beijing 100048, China

Sustainability, 2023, vol. 15, issue 4, 1-13

Abstract: Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in real-world dynamic scenarios. To improve the robustness and performance of SLAM systems in dynamic environments, this paper proposes a new RGB-D SLAM method for indoor dynamic scenes based on object detection. The method presented in this paper improves on the ORB-SLAM3 framework. First, we designed an object detection module based on YOLO v5 and relied on it to improve the tracking module of ORB-SLAM3 and the localization accuracy of ORB-SLAM3 in dynamic environments. The dense point cloud map building module was also included, which excludes dynamic objects from the environment map to create a static environment point cloud map with high readability and reusability. Full comparison experiments with the original ORB-SLAM3 and two representative semantic SLAM methods on the TUM RGB-D dataset show that: the method in this paper can run at 30+fps, the localization accuracy improved to varying degrees compared to ORB-SLAM3 in all four image sequences, and the absolute trajectory accuracy can be improved by up to 91.10%. The localization accuracy of the method in this paper is comparable to that of DS-SLAM, DynaSLAM and the two recent target detection-based SLAM algorithms, but it runs faster. The RGB-D SLAM method proposed in this paper, which combines the most advanced object detection method and visual SLAM framework, outperforms other methods in terms of localization accuracy and map construction in a dynamic indoor environment and has a certain reference value for navigation, localization, and 3D reconstruction.

Keywords: visual SLAM; ORB-SLAM3; object detection; dynamic environments; dense point cloud map (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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