通过自监督学习提升视觉里程计中的特征提取与跟踪稳定性。
Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
- 用任务特定反馈的自监督学习优化深度特征
- 在光照变化等复杂环境下保持稳定追踪性能
- 适合长期、大范围户外场景的视觉定位应用
基于视觉的定位虽取得显著进展，但在大规模、户外和长期设置下性能常因光照变化、动态场景和低纹理区域而下降，这些因素会削弱特征提取与追踪能力，进而影响运动估计精度。尽管基于学习的方法如SuperPoint和SuperGlue在特征覆盖和鲁棒性方面表现更优，但对分布外数据仍存在泛化问题。本文通过引入具有任务特定反馈的自监督学习，增强深度特征提取与追踪能力，使特征更具稳定性与信息量，从而提升在复杂环境下的泛化性与可靠性。
原文摘要 · Abstract (English)
Visual-based localization has made significant progress, yet its performance often drops in large-scale, outdoor, and long-term settings due to factors like lighting changes, dynamic scenes, and low-texture areas. These challenges degrade feature extraction and tracking, which are critical for accurate motion estimation. While learning-based methods such as SuperPoint and SuperGlue show improved feature coverage and robustness, they still face generalization issues with out-of-distribution data. We address this by enhancing deep feature extraction and tracking through self-supervised learning with task specific feedback. Our method promotes stable and informative features, improving generalization and reliability in challenging environments.
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