论文标题
BRM本地化：基于数值映射和无人机图像的匹配，在GNSS贬低的环境中的无人机本地化
BRM Localization: UAV Localization in GNSS-Denied Environments Based on Matching of Numerical Map and UAV Images
论文作者
论文摘要
本地化是在实际领域使用无人机（UAV）所需的最重要技术之一。目前，大多数无人机都使用GNS来估计其位置。最近，有一些攻击是针对使用GNSS的无人机的弱点，例如中断GNSS信号崩溃了无人机或发送假GNSS信号劫持无人机。为了避免这种情况，本文提出了一种算法，该算法涉及在GNSS贬低环境中无人机的本地化问题。我们提出了一种本地化方法，称为BRM（基于构建比率映射）本地化，用于通过将现有数值映射与无人机图像匹配的UAV。从无人机图像中提取建筑区域。在相应的图像框架中占据的建筑物的比率与数值图上的建筑信息进行计算并匹配。位置估计开始于几个km^2区域的范围，因此可以在不知道确切的初始坐标的情况下执行位置估计。仅免费提供的地图用于培训数据集并与地面真相匹配。最后，我们从UAV Flight获得了真正的无人机图像，IMU数据和GNSS数据，以表明所提出的方法可以比传统方法获得更好的性能。
Localization is one of the most important technologies needed to use Unmanned Aerial Vehicles (UAVs) in actual fields. Currently, most UAVs use GNSS to estimate their position. Recently, there have been attacks that target the weaknesses of UAVs that use GNSS, such as interrupting GNSS signal to crash the UAVs or sending fake GNSS signals to hijack the UAVs. To avoid this kind of situation, this paper proposes an algorithm that deals with the localization problem of the UAV in GNSS-denied environments. We propose a localization method, named as BRM (Building Ratio Map based) localization, for a UAV by matching an existing numerical map with UAV images. The building area is extracted from the UAV images. The ratio of buildings that occupy in the corresponding image frame is calculated and matched with the building information on the numerical map. The position estimation is started in the range of several km^2 area, so that the position estimation can be performed without knowing the exact initial coordinate. Only freely available maps are used for training data set and matching the ground truth. Finally, we get real UAV images, IMU data, and GNSS data from UAV flight to show that the proposed method can achieve better performance than the conventional methods.
