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.

翻译：无人飞行器（UAV）的精确地理定位对于搜救行动、电力线巡检和环境监测等多种户外应用至关重要。全球导航卫星系统（GNSS）信号易受干扰和欺骗的脆弱性，使得开发用于自主导航的额外鲁棒定位方法成为必要。视觉地理定位（VG）利用机载摄像头和参考卫星地图，为绝对定位提供了一种有前景的解决方案。具体而言，热成像地理定位（TG）依赖于热成像与卫星数据库之间的图像匹配，通过利用红外摄像头实现有效的夜间定位，从而脱颖而出。然而，当前TG方法的效率和有效性受到卫星地图密集采样以及热成像查询图像中几何噪声的阻碍。为克服这些挑战，本文提出STHN，一种新颖的无人机热成像地理定位方法，该方法采用由粗到精的深度单应性估计方法。即使在卫星图像与热成像之间仅有11%重叠率的挑战性条件下，且热成像纹理模糊、两种光谱均存在自相似模式的情况下，该方法仍能在无人机最后已知位置的512米半径范围内实现可靠的热成像地理定位。我们的研究显著提升了无人机热成像地理定位的性能，并增强了其在野外低能见度条件下对抗几何噪声影响的鲁棒性。代码将公开发布。