Det-SLAM: A semantic visual SLAM for highly dynamic scenes using Detectron2
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Abstract (arXiv)
According to experts, Simultaneous Localization and Mapping (SLAM) is an intrinsic part of autonomous robotic systems. Several SLAM systems with impressive performance have been invented and used during the last several decades. However, there are still unresolved issues, such as how to deal with moving objects in dynamic situations. Classic SLAM systems depend on the assumption of a static environment, which becomes unworkable in highly dynamic situations. Several methods have been presented to tackle this issue in recent years, but each has its limitations. This research combines the visual SLAM systems ORB-SLAM3 and Detectron2 to present the Det-SLAM system, which employs depth information and semantic segmentation to identify and eradicate dynamic spots to accomplish semantic SLAM for dynamic situations. Evaluation of public TUM datasets indicates that Det-SLAM is more resilient than previous dynamic SLAM systems and can lower the estimated error of camera posture in dynamic indoor scenarios.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 1 Oct 2022.
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References · 6
- 2017Mask R-CNNcited by 7,277
- 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
- 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 878
- 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 806
- 2018DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenescited by 134
- 2018DS-SLAM: A Semantic Visual SLAM towards Dynamic Environmentscited by 91
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