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About this paper
Fei Xue, Xin Wang, Junqiu Wang, Hongbin Zha
A deep visual odometry (VO) method integrates adaptive memory and a recurrent refining module to leverage global information and mitigate error accumulation, a common limitation in learning-based approaches. It demonstrates improved translational and rotational accuracy on KITTI and TUM-RGBD datasets, outperforming many learning-based methods and showing superior robustness in texture-less or abrupt motion scenarios compared to classic VO.
Key insights
- Explicitly integrating global information through an adaptive memory and a recurrent refinement mechanism is effective in significantly mitigating error accumulation in learning-based visual odometry.
- A two-level spatial-temporal attention mechanism enhances the refinement process by selectively focusing on co-visible and geometrically stable features from both current observations and stored global memories.
- Adaptive memory management, similar to keyframe selection in classic SLAM, is crucial for building a concise yet informative global map, balancing computational efficiency with the retention of long-term contextual information.
Summary by alphaXiv.
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