  We present a generic evidential grid mapping pipeline designed for imaging
sensors such as LiDARs and cameras. Our grid-based evidential model contains
semantic estimates for cell occupancy and ground separately. We specify the
estimation steps for input data represented by point sets, but mainly focus on
input data represented by images such as disparity maps or LiDAR range images.
Instead of relying on an external ground segmentation only, we deduce occupancy
evidence by analyzing the surface orientation around measurements. We conduct
experiments and evaluate the presented method using LiDAR and stereo camera
data recorded in real traffic scenarios. Our method estimates cell occupancy
robustly and with a high level of detail while maximizing efficiency and
minimizing the dependency to external processing modules.
