Dead reckoning and cartography using stereo vision for an autonomous car

Our main objective in the 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 (velocity and steering angle) can cause non-negligible errors, especially in situations where side slip or skidding occurs. We use stationary points in the scene to support the determination of our ego-motion. Two types of stationary objects are used: Firstly, stationary vertical landmarks such as traffic signs are used to compensate errors in our localization prediction. Secondly, lane markings measured in consecutive frames are used to compensate orientation errors. Preliminary results show that dead reckoning using stationary objects can vastly improve self-localization