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arXiv · 2207.08794

D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance

Xingyuan Yu·Weicai Ye·Xiyue Guo·Yuhang Ming·Jinyu Li·Hujun Bao·Zhaopeng Cui·Guofeng Zhang
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Abstract

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.

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Xingyuan Yu, Weicai Ye, Xiyue Guo, Yuhang Ming, Jinyu Li, Hujun Bao, Zhaopeng Cui, Guofeng Zhang. 2022-07-18. D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance. https://arxiv.org/abs/2207.08794

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