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

arXiv (Cornell University)

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.

Authors 3

1. Sai Puneeth Reddy Gottam
2. Haoming Zhang Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

3. Eivydas Keras Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen