Real-Time Vehicle Ego-Motion Using Stereo Pairs and Particle Filters

Abstract. 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 do-main or in 3D space, our proposed approach directly estimates the un-known 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 pro-posed 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.