Real-time vehicle ego-motion using stereo pairs and particle filters

This paper presents a direct and stochastic technique for real time estimation of on board camera position and orientation-the ego-motion problem. An on board stereo vision system is used. Unlike existing works, which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the brightness of a stream of stereo pairs. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the dynamics. The proposed technique can be used in driving assistance applications as well as in augmented reality applications. Experimental results and comparisons on urban environments with different road geometries are presented.