Self-Supervised Classification for Planetary Rover Terrain Sensing

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.