FoundLoc: Vision-based Onboard Aerial Localization in the Wild
Abstract
Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of a known initial pose. In this paper, we developed a GNSS-denied localization approach for UAVs that harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition (VPR) using a foundation model. This paper presents a novel vision-based pipeline that works exclusively with a nadir-facing camera, an Inertial Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate localization in varied environments and conditions. Our system demonstrated average localization accuracy within a -meter range, with a minimum error below meter, under real-world conditions marked by drastic changes in environmental appearance and with no assumption of the vehicle's initial pose. The method is proven to be effective and robust, addressing the crucial need for reliable UAV localization in GNSS-denied environments, while also being computationally efficient enough to be deployed on resource-constrained platforms.
Cite
@article{arxiv.2310.16299,
title = {FoundLoc: Vision-based Onboard Aerial Localization in the Wild},
author = {Yao He and Ivan Cisneros and Nikhil Keetha and Jay Patrikar and Zelin Ye and Ian Higgins and Yaoyu Hu and Parv Kapoor and Sebastian Scherer},
journal= {arXiv preprint arXiv:2310.16299},
year = {2023}
}