Accurate geo-localization of Unmanned Aerial Vehicles (UAVs) is crucial for 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 nighttime 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, 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% size ratio between thermal and satellite images, despite the presence of indistinct textures and self-similar patterns. We further show how our research significantly enhances UAV thermal geo-localization performance and robustness against geometric noises under low-visibility conditions in the wild. The code is made publicly available.

翻译：无人机（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，使得开发额外的鲁棒定位方法以实现自主导航成为必要。视觉地理定位（VG）利用机载相机和参考卫星地图，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，通过利用红外相机实现有效的夜间定位而脱颖而出。然而，当前TG方法的效率和有效性受到卫星地图上的密集采样以及热查询图像中几何噪声的阻碍。为了克服这些挑战，我们提出了STHN，一种新颖的无人机热成像地理定位方法，该方法采用了一种由粗到精的深度单应性估计方法。即使在热图像与卫星图像尺寸比仅为11%的挑战性条件下，并且存在纹理模糊和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。我们进一步展示了我们的研究如何显著提升无人机在野外低能见度条件下的热成像地理定位性能以及对几何噪声的鲁棒性。代码已公开提供。