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

arXiv: https://arxiv.org/abs/2509.08333 · HTML: https://mlsanity.com/paper/2509.08333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation](https://mlsanity.com/paper/2403.06341/) (2024)
- [Reinforcement Learning Meets Visual Odometry](https://mlsanity.com/paper/2407.15626/) (2024)
- [Pohang Canal Dataset: A Multimodal Maritime Dataset for Autonomous Navigation in Restricted Waters](https://mlsanity.com/paper/2303.05555/) (2023)
- [GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization](https://mlsanity.com/paper/2309.11134/) (2023)

## Abstract (arXiv)

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.