Accurate geo-localization of Unmanned Aerial Vehicles (UAVs) is crucial for a
variety of outdoor applications including search and rescue operations, power
line inspections, and environmental monitoring. The vulnerability of Global
Navigation Satellite Systems (GNSS) signals to interference and spoofing
necessitates the development of additional robust localization methods for
autonomous navigation. Visual Geo-localization (VG), leveraging onboard cameras
and reference satellite maps, offers a promising solution for absolute
localization. Specifically, Thermal Geo-localization (TG), which relies on
image-based matching between thermal imagery with satellite databases, stands
out by utilizing infrared cameras for effective night-time localization.
However, the efficiency and effectiveness of current TG approaches, are
hindered by dense sampling on satellite maps and geometric noises in thermal
query images. To overcome these challenges, in this paper, we introduce STHN, a
novel UAV thermal geo-localization approach that employs a coarse-to-fine deep
homography estimation method. This method attains reliable thermal
geo-localization within a 512-meter radius of the UAV's last known location
even with a challenging 11% overlap between satellite and thermal images,
despite the presence of indistinct textures in thermal imagery and self-similar
patterns in both spectra. Our research significantly enhances UAV thermal
geo-localization performance and robustness against the impacts of geometric
noises under low-visibility conditions in the wild. The code will be made
publicly available.

STHN 是一种新的无人机热定位方法，通过粗糙到精细的深度单应性估计方法，在低能见度条件下可可在无人机上次已知位置的 512 米范围内实现可靠的热定位，克服了热影像模糊纹理和自相似模式的困扰，显著提高了无人机热定位性能和鲁棒性。