Stereo vision-based localization for hexapod walking robots operating in rough terrains

Abstract

This paper concerns the self-localization problem of a hexapod walking robot operating in rough terrains. Given that legged robots exhibit higher terrain passability than wheeled or tracked platforms when operating in harsh environments, they constitute a challenge for the localization techniques because the camera motion between consecutive frames can be arbitrary due to the motion gait and terrain irregularities. In this paper, we present and evaluate an inertially assisted Stereo Parallel Tracking and Mapping (S-PTAM) method deployed on a hexapod crawling robot in a rough terrain. The considered deployment scenario is motivated by autonomous navigation in an unknown environment in an open loop fashion. The reported results and comparison with an existing RGB-D SLAM technique show the feasibility of the proposed approach and its suitability for navigation of crawlers in harsh environments.