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arXiv · 2509.08333

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry

Sai Puneeth Reddy Gottam·Haoming Zhang·Eivydas Keras
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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.

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Sai Puneeth Reddy Gottam, Haoming Zhang, Eivydas Keras. 2025-09-10. Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry. https://arxiv.org/abs/2509.08333

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