Self-Supervised Classification for Planetary Rover Terrain Sensing

Christopher A. Brooks, K. Iagnemma
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引用次数: 66

Abstract

Autonomous mobility in rough terrain is key to enabling increased science data return from planetary rover missions. Current terrain sensing and path planning approaches can be used to avoid geometric hazards, such as rocks and steep slopes, but are unable to remotely identify and avoid non-geometric hazards, such as loose sand in which a rover may become entrenched. This paper proposes a self-supervised classification approach to learning the visual appearance of terrain classes which relies on vibration-based sensing of wheel-terrain interaction to identify these terrain classes. Experimental results from a four-wheeled rover in Mars analog terrain demonstrate the potential for this approach.
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行星漫游者地形感知的自监督分类
在崎岖地形上的自主机动能力是提高行星探测器任务科学数据返回率的关键。目前的地形传感和路径规划方法可以用来避免几何危险,如岩石和陡坡,但无法远程识别和避免非几何危险,如松散的沙子,漫游者可能会陷入其中。本文提出了一种基于车轮-地形相互作用的振动感知来识别地形类视觉外观的自监督分类方法。在火星模拟地形上的四轮探测车的实验结果证明了这种方法的潜力。
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