D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866916363534073856 |
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| author | Yu, Xingyuan Ye, Weicai Guo, Xiyue Ming, Yuhang Li, Jinyu Bao, Hujun Cui, Zhaopeng Zhang, Guofeng |
| author_facet | Yu, Xingyuan Ye, Weicai Guo, Xiyue Ming, Yuhang Li, Jinyu Bao, Hujun Cui, Zhaopeng Zhang, Guofeng |
| contents | In this paper, we introduce a self-supervised deep SLAM method that robustly operates in dynamic scenes while accurately identifying dynamic components. Our method leverages a dual-flow representation for static flow and dynamic flow, facilitating effective scene decomposition in dynamic environments. We propose a dynamic update module based on this representation and develop a dense SLAM system that excels in dynamic scenarios. In addition, we design a self-supervised training scheme using DINO as a prior, enabling label-free training. Our method achieves superior accuracy compared to other self-supervised methods. It also matches or even surpasses the performance of existing supervised methods in some cases. All code and data will be made publicly available upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_08794 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance Yu, Xingyuan Ye, Weicai Guo, Xiyue Ming, Yuhang Li, Jinyu Bao, Hujun Cui, Zhaopeng Zhang, Guofeng Computer Vision and Pattern Recognition Robotics In this paper, we introduce a self-supervised deep SLAM method that robustly operates in dynamic scenes while accurately identifying dynamic components. Our method leverages a dual-flow representation for static flow and dynamic flow, facilitating effective scene decomposition in dynamic environments. We propose a dynamic update module based on this representation and develop a dense SLAM system that excels in dynamic scenarios. In addition, we design a self-supervised training scheme using DINO as a prior, enabling label-free training. Our method achieves superior accuracy compared to other self-supervised methods. It also matches or even surpasses the performance of existing supervised methods in some cases. All code and data will be made publicly available upon acceptance. |
| title | D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2207.08794 |