We propose a novel method for geolocalizing Unmanned Aerial Vehicles (UAVs) in environments lacking Global Navigation Satellite Systems (GNSS). Current state-of-the-art techniques employ an offline-trained encoder to generate a vector representation (embedding) of the UAV's current view, which is then compared with pre-computed embeddings of geo-referenced images to determine the UAV's position. Here, we demonstrate that the performance of these methods can be significantly enhanced by preprocessing the images to extract their edges, which exhibit robustness to seasonal and illumination variations. Furthermore, we establish that utilizing edges enhances resilience to orientation and altitude inaccuracies. Additionally, we introduce a confidence criterion for localization. Our findings are substantiated through synthetic experiments.

翻译：我们提出了一种在缺乏全球导航卫星系统的环境中对无人机进行地理定位的新型方法。当前最先进的技术采用离线训练的编码器生成无人机当前视角的向量表示（嵌入），随后将其与预计算的地理参考图像嵌入进行比对以确定无人机位置。本研究表明，通过预处理图像提取其边缘信息，可以显著提升这些方法的性能——边缘特征对季节变化和光照变化具有鲁棒性。进一步地，我们证实了利用边缘能够增强对朝向和高度误差的适应性。此外，我们引入了定位的置信度判据。上述结论通过合成实验得到了验证。