Improved Rover State Estimation in Challenging Terrain

Abstract

Given ambitious mission objectives and long delay times between command-uplink/data-downlink sessions, increased autonomy is required for planetary rovers. Specifically, NASA's planned 2003 and 2005 Mars rover missions must incorporate increased autonomy if their desired mission goals are to be realized. Increased autonomy, including autonomous path planning and navigation to user designated goals, relies on good quality estimates of the rover's state, e.g., its position and orientation relative to some initial reference frame. The challenging terrain over which the rover will necessarily traverse tends to seriously degrade a dead-reckoned state estimate, given severe wheel slip and/or interaction with obstacles. In this paper, we present the implementation of a complete rover navigation system. First, the system is able to adaptively construct semi-sparse terrain maps based on the current ground texture and distances to possible nearby obstacles. Second, the rover is able to match successively constructed terrain maps to obtain a vision-based state estimate which can then be fused with wheel odometry to obtain a much improved state estimate. Finally the rover makes use of this state estimate to perform autonomous real-time path planning and navigation to user designated goals. Reactive obstacle avoidance is also implemented for roaming in an environment in the absence of a user designated goal. The system is demonstrated in soft soil and relatively dense rock fields, achieving state estimates that are significantly improved with respect to dead reckoning alone (e.g., 0.38 m mean absolute error vs. 1.34 m), and successfully navigating in multiple trials to user designated goals.