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Landmark recognition for localisation and navigation of aerial vehicles

机译:地标识别空中车辆本地化和航航

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Work has been undertaken at the University of Wales, Aberystwyth in the area of localisation and navigation of aerial vehicles (aerobots) in large unstructured environments (i.e. natural outdoors). The localisation and navigation method presented in this paper was developed for planetary exploration with an emphasis on Mars, but could also be used on Earth. Mars has an atmosphere, which is dense enough to allow the use of aerobots, and the Mars Orbiter Laser Altimeter (MOLA) [1][2] has provided the low resolution topographical map of the surface. The MOLA data [3] has provided the scenery for Flight-Gear [4] an open source flight simulator, which provided the environment within which all the localisation and navigation experiments have been conducted. Localisation and navigation has been achieved by extracting naturally occurring surface features (landmarks i.e. peaks, ridges, channels etc.) from the topographical maps. By categorising the surface by its features, then by matching these features in a high resolution topographical map, generated onboard the aerobot, with the same features in the low resolution global map, (e.g. MOLA data) a position estimate is obtained. Once the aerobot has localised, navigation to desired positions can be achieved using a combination of a feature path (feature navigation) and inertial navigation methods. This paper presents the results obtained from the localisation and navigation phases, from the point at which an aerobot obtains topographical maps of the surface, analyse them for features, estimates its position and orientation, to the point of navigating to the desired sites of scientific interest.
机译:在大型非结构化环境中的航空车辆(Aerobots航空公司的本地化和航行领域,在威尔士大学,在大型非结构化环境中进行了工作(即自然户外)。本文提出的本地化和导航方法是为行星勘探开发的,强调火星,但也可以在地球上使用。火星具有足够密集的气氛,以允许使用Aerobots,火星轨道激光高度计(Mola)[1] [2]提供了表面的低分辨率地形图。 MOLA数据[3]为飞行齿轮的风景提供了一种开源飞行模拟器,该模拟器提供了所有本地化和导航实验的环境。通过从地形图中提取自然发生的表面特征(地标I.峰值,频道等)来实现本地化和导航。通过通过其特征对表面进行分类,然后通过将这些特征匹配在高分辨率地形图中,在机架上生成的,在低分辨率全球地图中具有相同的特征,(例如Mola数据)获得了位置估计。一旦Aerobot局部化,可以使用特征路径(特征导航)和惯性导航方法的组合来实现到所需位置的导航。本文介绍了从本地化和导航阶段获得的结果,从Aerobot获得地形地图的地形,分析它们的特征,估计其位置和方向,以导航到所需的科学兴趣的所需站点。

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