Considering the accelerated development of Unmanned Aerial Vehicles (UAVs) applications in both industrial and research scenarios, there is an increasing need for localizing these aerial systems in non-urban environments, using GNSS-Free, vision-based methods. Our paper proposes a vision-based localization algorithm that utilizes deep features to compute geographical coordinates of a UAV flying in the wild. The method is based on matching salient features of RGB photographs captured by the drone camera and sections of a pre-built map consisting of georeferenced open-source satellite images. Experimental results prove that vision-based localization has comparable accuracy with traditional GNSS-based methods, which serve as ground truth. Compared to state-of-the-art Visual Odometry (VO) approaches, our solution is designed for long-distance, high-altitude UAV flights. Code and datasets are available at https://github.com/TIERS/wildnav.
@article{arxiv.2210.09727,
title = {Vision-based GNSS-Free Localization for UAVs in the Wild},
author = {Marius-Mihail Gurgu and Jorge Peña Queralta and Tomi Westerlund},
journal= {arXiv preprint arXiv:2210.09727},
year = {2022}
}
Comments
6 pages, 6 figures, submitted to the International Conference on Mechanical Engineering and Robotics Research 2022