提出新方法同时评估热成像定位中的数据与模型不确定性。
UASTHN: Uncertainty-Aware Deep Homography Estimation for UAV Satellite-Thermal Geo-localization
- 用裁剪测试时增强法分析图像一致性，测量数据不确定性。
- 结合深度集成实现高效模型不确定性估计，性能相当但更快。
- 适合在弱纹理或图像失真时仍需可靠定位的无人机系统使用。
地理定位是无人机导航系统中确保户外环境精确绝对定位的关键。为应对GPS信号中断或光照不足的问题，热成像地理定位（TG）通过将航拍热图像与参考卫星地图对齐来确定无人机位置。然而，现有TG方法缺乏输出的不确定性度量，在无纹理、图像损坏、自相似或过时的卫星图、几何噪声或热图像超出卫星图范围等条件下，系统鲁棒性下降。为此，本文提出UASTHN，一种用于热成像地理定位中深度单应性估计的不确定性感知方法。我们引入基于裁剪的测试时增强（CropTTA）策略，利用裁剪视图间的单应性一致性来有效测量数据不确定性。该方法与深度集成（DE）结合，用于模型不确定性估计，性能相当但效率更高，且可无缝集成到任意深度单应性估计模型中。在多个深度单应性模型上的大量实验表明，CropTTA在热成像定位任务中兼具有效性与高效性。失败案例分析证实其在挑战性条件下的可靠性提升。最后，我们展示了结合CropTTA与DE实现数据与模型不确定性综合评估的能力。本研究为定位与不确定性估计的交叉领域提供了深刻洞见。代码与模型已公开。
原文摘要 · Abstract (English)
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outputs, compromising system robustness in the presence of textureless or corrupted thermal images, self-similar or outdated satellite maps, geometric noises, or thermal images exceeding satellite maps.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.
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