Rough-terrain mobile robot localization using stereovision

Mobile robots are increasingly being used in high-risk rough terrain situations, such as reconnaissance, planetary exploration, safety and rescue applications. Conventional localization algorithms are not well suited to rough terrain, since sensor drift and the dynamic effects occurring at wheel-terrain interface, such as slipping and sinkage, largely compromise their accuracy. In this paper, we follow a novel approach for 6-DoF ego-motion estimation, using stereovision. It integrates image intensity information and 3D stereo data within an Iterative Closest Point (ICP) scheme. Neither a-priori knowledge of the motion and the terrain properties nor inputs from other sensors are required, while the only assumption is that the scene always contains visually distinctive features, which can be tracked over subsequent stereo pairs. This generates what is usually referred to as visual odometry. The paper details the various steps of the algorithm and presents the results of experimental tests performed with an all-terrain rover, proving the method to be effective and robust.