Transforming Camera Geometry to A Virtual Downward-Looking Camera: Robust Ego-Motion Estimation and Ground-Layer Detection

This paper presents a robust method to solve the two coupled problems: ground layer detection and vehicle egomotion estimation, which appear in visual navigation. We virtually rotate the camera to the downward-looking pose in order to exploit the fact that the vehicle motion is roughly constrained to be planar motion on the ground. This camera geometry transformation, together with planar motion constraint, will: 1) eliminate the ambiguity between rotational and translational ego-motion parameters, and 2) improve the Hessian matrix condition in the direct motion estimation process. The virtual downward-looking camera enables us to estimate the planar ego-motions even for small image patches. Such local measurements are then combined together, by a robust weighting scheme based on both ground plane geometry and motion compensated intensity residuals, for a global ego-motion estimation and ground plane detection. We demonstrate the effectiveness of our method by experiments on both synthetic and real data.