This paper proposes a novel approach to map-based navigation system for unmanned aircraft. The proposed system attempts label-to-label matching, not image-to-image matching between aerial images and a map database. By using semantic segmentation, the ground objects are labelled and the configuration of the objects is used to find the corresponding location in the map database. The use of the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern matching problem. This paper proposes a pattern matching algorithm which does not require altitude information or a camera model to estimate the absolute horizontal position. The feasibility analysis with simulated images shows the proposed map-based navigation can be realized with the proposed pattern matching algorithm and it is able to provide positions given the labelled objects.

翻译：本文建议对无人驾驶航空器的基于地图的导航系统采取一种新颖的方法。 提议的系统试图在空中图像和地图数据库之间进行标签到标签的匹配, 而不是图像到图像的图像到图像的匹配。 通过使用语义分隔, 将地面物体贴上标签, 并使用天体的配置来寻找地图数据库中的相应位置。 使用深层次学习技术作为提取高层次特征的工具, 将基于图像的定位问题降低为模式匹配问题 。 本文建议一种模式匹配算法, 不需要高度信息或相机模型来估计绝对水平位置 。 模拟图像的可行性分析显示, 拟议的基于地图的导航可以通过拟议的模式匹配算法来实现, 并且能够提供基于标签对象的位置 。