Computer Vision Methods for Improved Mobile Robot State Estimation in Challenging Terrains

Abstract

External perception based on vision plays a critical role in developing improved and robust localization algorithms, as well as gaining important information about the vehicle and the terrain it is traversing. This paper presents two novel methods for rough terrain-mobile robots, using visual input. The first method consists of a stereovision algorithm for real-time 6DoF ego-motion estimation. It integrates image intensity information and 3D stereo data in the well-known Iterative Closest Point (ICP) scheme. Neither a-priori knowledge of the motion 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 second method aims at estimating the wheel sinkage of a mobile robot on sandy soil, based on edge detection strategy. A semi-empirical model of wheel sinkage is also presented referring to the classical terramechanics theory. Experimental results obtained with an all-terrain mobile robot and with a wheel sinkage test bed are presented to validate our approach. It is shown that the proposed techniques can be integrated in control and planning algorithms to improve the performance of ground vehicles operating in uncharted environments.