Cartography and dead reckoning using stereo vision for an autonomous car

Our main objective in this paper is to perform a cartography of a road scene into a reference frame at rest, where 3D measurements delivered by on-board sensors serve as input. The main sensors of our autonomous vehicle are two CCD cameras. Their pictures are combined using stereopsis to generate 3D data. We need dead reckoning to properly associate 3D data among the frames. This necessitates us to obtain a precise ego-motion estimation. Dead reckoning using only standard vehicle odometry can cause nonnegligible errors. We use stationary points in the scene to support the determination of our ego-motion. Two types of stationary objects are used: vertical landmarks such as traffic signs and lane markings are used to compensate positioning errors. Preliminary results show that cartography as proposed in this paper is beneficial to detect stationary objects but needs further work for fast moving objects.