Geo-localization is an essential component of Unmanned Aerial Vehicle (UAV) navigation systems to ensure precise absolute self-localization in outdoor environments. To address the challenges of GPS signal interruptions or low illumination, Thermal Geo-localization (TG) employs aerial thermal imagery to align with reference satellite maps to accurately determine the UAV's location. However, existing TG methods lack uncertainty measurement in their outputs, compromising system robustness in the presence of textureless or corrupted thermal images, self-similar or outdated satellite maps, geometric noises, or thermal images exceeding satellite maps. To overcome these limitations, this paper presents UASTHN, a novel approach for Uncertainty Estimation (UE) in Deep Homography Estimation (DHE) tasks for TG applications. Specifically, we introduce a novel Crop-based Test-Time Augmentation (CropTTA) strategy, which leverages the homography consensus of cropped image views to effectively measure data uncertainty. This approach is complemented by Deep Ensembles (DE) employed for model uncertainty, offering comparable performance with improved efficiency and seamless integration with any DHE model. Extensive experiments across multiple DHE models demonstrate the effectiveness and efficiency of CropTTA in TG applications. Analysis of detected failure cases underscores the improved reliability of CropTTA under challenging conditions. Finally, we demonstrate the capability of combining CropTTA and DE for a comprehensive assessment of both data and model uncertainty. Our research provides profound insights into the broader intersection of localization and uncertainty estimation. The code and models are publicly available.

翻译：地理定位是无人机导航系统的关键组成部分，旨在确保无人机在室外环境中实现精确的绝对自定位。为解决GPS信号中断或低光照条件下的挑战，热成像地理定位利用航空热成像图像与参考卫星地图进行配准，以准确确定无人机的位置。然而，现有TG方法在其输出中缺乏不确定性度量，导致系统在面临纹理缺失或损坏的热图像、自相似或过时的卫星地图、几何噪声或超出卫星地图范围的热图像时，鲁棒性受到损害。为克服这些局限，本文提出UASTHN，一种面向TG应用中深度单应性估计任务的不确定性估计新方法。具体而言，我们引入了一种新颖的基于裁剪的测试时增强策略，该策略利用裁剪图像视图的单应性一致性来有效度量数据不确定性。该方法与用于模型不确定性的深度集成方法相结合，在保持可比性能的同时提高了效率，并可无缝集成到任何DHE模型中。在多个DHE模型上的大量实验证明了CropTTA在TG应用中的有效性和高效性。对检测到的失败案例的分析突显了CropTTA在挑战性条件下可靠性的提升。最后，我们展示了结合CropTTA与DE以全面评估数据和模型不确定性的能力。本研究为定位与不确定性估计的更广泛交叉领域提供了深刻见解。代码与模型均已公开。