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.
@article{arxiv.2509.08333,
title = {Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry},
author = {Sai Puneeth Reddy Gottam and Haoming Zhang and Eivydas Keras},
journal= {arXiv preprint arXiv:2509.08333},
year = {2025}
}
Comments
This short paper has been accepted as a workshop paper at European Conference on Mobile Robots 2025