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Meta-Image Navigation Augmenters for Unmanned Aircraft Systems (MINA for UAS)

机译：适用于无人机系统的元图像导航增强器（适用于UAS的MINA）

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GPS is a critical sensor for Unmanned Aircraft Systems (UASs) due to its accuracy, global coverage and small hardware footprint, but is subject to denial due to signal blockage or RF interference. When GPS is unavailable, position, velocity and attitude (PVA) performance from other inertial and air data sensors is not sufficient, especially for small UASs. Recently, image-based navigation algorithms have been developed to address GPS outages for UASs, since most of these platforms already include a camera as standard equipage. Performing absolute navigation with real-time aerial images requires georeferenced data, either images or landmarks, as a reference. Georeferenced imagery is readily available today, but requires a large amount of storage, whereas collections of discrete landmarks are compact but must be generated by pre-processing. An alternative, compact source of georeferenced data having large coverage area is open source vector maps from which meta-objects can be extracted for matching against real-time acquired imagery. We have developed a novel, automated approach called MINA (Meta Image Navigation Augmenters), which is a synergy of machine-vision and machine-learning algorithms for map aided navigation. As opposed to existing image map matching algorithms, MINA utilizes publicly available open-source geo-referenced vector map data, such as OpenStreetMap, in conjunction with real-time optical imagery from an on-board, monocular camera to augment the UAS navigation computer when GPS is not available. The MINA approach has been experimentally validated with both actual flight data and flight simulation data and results are presented in the paper.
机译：GPS由于其准确性，全球覆盖范围和较小的硬件占用空间而成为无人飞机系统（UAS）的关键传感器，但由于信号阻塞或RF干扰而遭到拒绝。当GPS不可用时，其他惯性和空中数据传感器的位置，速度和姿态（PVA）性能不足，特别是对于小型UAS。最近，已经开发出基于图像的导航算法来解决UAS的GPS中断问题，因为这些平台中的大多数平台已经包含了摄像头作为标准装备。要对实时航拍图像执行绝对导航，需要将地理参考数据（图像或地标）作为参考。如今，地理参考图像已经可以使用，但是需要大量存储，而离散地标的集合非常紧凑，但是必须通过预处理来生成。具有大覆盖区域的地理参考数据的替代的紧凑源是开源矢量地图，可以从中提取元对象以与实时获取的图像进行匹配。我们已经开发了一种新颖的自动化方法，称为MINA（元图像导航增强器），它是机器视觉和地图学习导航的机器学习算法的结合。与现有的图像地图匹配算法相反，MINA利用公开可用的开源地理参考矢量地图数据（例如OpenStreetMap），结合来自车载单目摄像头的实时光学图像，以增强UAS导航计算机的性能GPS不可用。 MINA方法已经通过实际飞行数据和飞行模拟数据进行了实验验证，结果在本文中进行了介绍。

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