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Mars rovers localization by matching local horizon to surface digital elevation models

机译:火星通过将局部地平线与地面数字高程模型匹配来进行本地化

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In this work we have performed a sensitivity analysis of the Visual Position Estimator for Rover (VIPER) algorithm using data and images provided by NASA MER exploration rovers and NASA Mars Reconnaissance Orbiter. The algorithm retrieves the rover camera position and orientation relative to a Digital Elevation Model by comparing the skyline extracted from a panoramic image captured by the rover and a set of skylines simulated on a template positions grid over the DEM. This algorithm can be used to initialize the rover position after landing in a Mars Body-Fixed Frame and as verification of rover guidance and navigation outputs. In order to test the algorithm performances we have processed data and images provided by NASA Mars Exploration Rover PANCAM and DEM provided by NASA Mars Reconnaissance Orbiter HiRISE telescope. The sensitivity analysis has been performed by varying DEM resolution and template positions density. In the tested cases we show that this localization technique achieves an error up to 50 [m], thus it is possible to decrease the position uncertainty estimated with other localization techniques, like the Entry Descent and Landing estimation.
机译:在这项工作中,我们使用了NASA MER探测漫游器和NASA火星侦察轨道器提供的数据和图像,对流动站视觉位置估计器(VIPER)算法进行了敏感性分析。该算法通过比较从流动站捕获的全景图像中提取的天际线和在DEM上模板位置网格上模拟的一组天际线,来检索流动站相机相对于数字高程模型的位置和方向。该算法可用于在降落在火星人体固定框架中后初始化流动站位置,并用作流动站引导和导航输出的验证。为了测试算法性能,我们处理了NASA火星探测漫游者PANCAM提供的数据和图像,以及NASA火星侦察轨道Hiris望远镜提供的DEM。灵敏度分析是通过更改DEM分辨率和模板位置密度来进行的。在测试案例中,我们证明了这种定位技术可实现高达50 [m]的误差,因此有可能减少采用其他定位技术(如进入下降和着陆估算)估算出的位置不确定性。

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