利用边缘检测和神经网络实现更好的无人机定位
Leveraging edge detection and neural networks for better UAV localization
AI总结：
针对GNSS缺失环境，本文提出在无人机视觉地理定位中引入边缘检测预处理，以增强对季节、光照、方向和高度变化的鲁棒性，并给出定位置信度准则。
摘要：
我们提出了一种在缺乏全球导航卫星系统（GNSS）的环境中对无人机（UAV）进行地理定位的新方法。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图的向量表示（嵌入），随后将其与地理参考图像的预计算嵌入进行比较，以确定无人机的位置。本文证明，通过预处理图像以提取其边缘，可显著增强这些方法的性能，因为边缘对季节和光照变化具有鲁棒性。此外，我们证实利用边缘可提高对方向和高度误差的适应能力。我们还引入了一种定位置信度准则。研究结果通过合成实验得到验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
