Mobile robots require an accurate environment perception to plan intelligent maneuvers and avoid collisions. This thesis presents a novel multi sensor environment estimation strategy that fully combines tracking moving objects and mapping the static environment. The basic idea is to fuse and accumulate measurement data by a dynamic occupancy grid model, whereas moving objects are extracted subsequently based on that generic low-level grid representation. Overall, this work results in a robust and consistent estimation of arbitrary objects and obstacles, which is demonstrated in the context of autonomous driving in complex unstructured environments.
