{"ID":2872474,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.08333","arxiv_id":"2509.08333","title":"Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry","abstract":"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.","short_abstract":"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 estimatio...","url_abs":"https://arxiv.org/abs/2509.08333","url_pdf":"https://arxiv.org/pdf/2509.08333v1","authors":"[\"Sai Puneeth Reddy Gottam\",\"Haoming Zhang\",\"Eivydas Keras\"]","published":"2025-09-10T07:15:43Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\"]","methods":"[]","has_code":false}