Evaluation of Image Matching Methods for Visual Odometry on UAVs
TL;DR
This paper evaluates recent state-of-the-art image matching methods for the task of VO for UAV position tracking, with a downwards-facing camera, on a synthetic dataset, and finds that while the best results are generated by the recent RoMa matcher, SIFT features can outperform some recent state-of-the-art methods.
Abstract
Unmanned aerial vehicles (UAVs) are becoming a powerful tool for many environmental monitoring and transport applications. Yet, their reliance on Global Navigation Satellite System (GNSS) technology for navigation makes them susceptible to catastrophic failures in scenarios where the positioning signal is unavailable or disrupted. This work explores Visual Odometry (VO) as a crucial navigation component. Recently, numerous deep-learning-based methods for image matching have been proposed that are yet to be implemented in a fully-fledged VO system. In this paper, we evaluate recent state-of-the-art image matching methods for the task of VO for UAV position tracking, with a downwards-facing camera, on our synthetic dataset, and find that while the best results are generated by the recent RoMa matcher, SIFT features can outperform some recent state-of-the-art.