Abstract
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.
Abstract (translated)
我们提出了一个用于在缺乏全球导航卫星系统（GNSS）的环境中定位自主飞行器（UAV）的新方法。目前最先进的技术使用离线训练的编码器生成UAV当前视图的向量表示（嵌入），然后将其与预计算的地理参考图像的嵌入进行比较，以确定UAV的位置。在这里，我们证明了这些方法的性能可以通过预处理图像来提取其边缘得到显著提高，这些边缘表现出对季节性和光照变化具有较强的鲁棒性。此外，我们还证明了利用边缘可以增强对方向和高度不准确性的抵抗力。此外，我们引入了一个用于定位的置信度标准。我们的研究结果通过仿真实验得到了证实。
URL
https://arxiv.org/abs/2404.06207
