论文标题
野外无人机的基于视觉的无GNSS本地化
Vision-based GNSS-Free Localization for UAVs in the Wild
论文作者
论文摘要
考虑到在工业和研究方案中无人驾驶汽车（UAV）应用的加速开发，使用基于GNSS的，基于视觉的方法，越来越需要将这些空中系统定位在非城市环境中。我们的论文提出了一种基于视觉的本地化算法，该算法利用深层特征来计算野外无人机飞行的地理坐标。该方法基于无人机摄像头捕获的RGB照片的显着特征，以及由地图的预制地图的部分组成的部分，该图是由地理参考的开源卫星图像组成的。实验结果证明，基于视觉的本地化与基于GNSS的传统方法具有可比的精度，这些方法是基础真理。与最先进的视觉探针（VO）方法相比，我们的解决方案是为长途，高空无人机飞行而设计的。代码和数据集可在https://github.com/tiers/wildnav上找到。
Considering the accelerated development of Unmanned Aerial Vehicles (UAVs) applications in both industrial and research scenarios, there is an increasing need for localizing these aerial systems in non-urban environments, using GNSS-Free, vision-based methods. Our paper proposes a vision-based localization algorithm that utilizes deep features to compute geographical coordinates of a UAV flying in the wild. The method is based on matching salient features of RGB photographs captured by the drone camera and sections of a pre-built map consisting of georeferenced open-source satellite images. Experimental results prove that vision-based localization has comparable accuracy with traditional GNSS-based methods, which serve as ground truth. Compared to state-of-the-art Visual Odometry (VO) approaches, our solution is designed for long-distance, high-altitude UAV flights. Code and datasets are available at https://github.com/TIERS/wildnav.
