  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 $20$-meter range, with a minimum error
below $1$ 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.
