Real-time vision based ego-motion estimation for outdoor mobile robot

Abstract

Ego-motion estimation is a key issue of outdoor mobile robot navigation, especially in the demand of moving object tracking. In this paper, we proposed correspondence based method, which apply iterative closest point (ICP) algorithm to match feature points on ground plane. However the outliers in the scene contribute false measurement for estimation, thus we introduce a stereovision-based method to detect free-space on the road plane. Then we extract edge points in the free-space as primitives, which avoid the limit of the rigid scene hypothesis. This method has been tested on THMR-V (Tsinghua mobile robot V), which is an outdoor mobile robot developed by Tsinghua University. Through various experiments we successfully demonstrate its real-time performance and high robustness.