Knowing the position and orientation of an UAV without GNSS is a critical functionality in autonomous operations of UAVs. Vision-based localization on a known map can be an effective solution, but it is burdened by two main problems: places have different appearance depending on weather and season and the perspective discrepancy between the UAV camera image and the map make matching hard. In this work, we propose a localization solution relying on matching of UAV camera images to georeferenced orthophotos with a trained CNN model that is invariant to significant seasonal appearance difference (winter-summer) between the camera image and map. We compare the convergence speed and localization accuracy of our solution to three other commonly used methods. The results show major improvements with respect to reference methods, especially under high seasonal variation. We finally demonstrate the ability of the method to successfully localize a real UAV, showing that the proposed method is robust to perspective changes.

翻译：了解没有全球导航卫星系统的无人驾驶航空器的位置和方向是无人驾驶航空器自主运行中的一个关键功能。已知地图上的基于愿景的定位可能是有效的解决办法,但有两个主要问题:视天气和季节不同,无人驾驶航空器相机图像与地图相匹配的视角差异很大。在这项工作中,我们提出一个本地化解决方案,其依据是将无人驾驶航空器相机图像与地理参照的地球光伏成像相匹配,并配有经过培训的CNN模型,该模型对摄影图像和地图之间的季节性外观差异(冬季夏季)有很大差异。我们将我们解决方案的趋同速度和本地化精度与其他三种常用方法进行比较。结果显示在参考方法方面有重大改进,特别是在高度季节性变异的情况下。我们最后展示了该方法成功定位真实的无人驾驶航空器的能力,表明拟议方法对于观察变化十分健全。