  A detailed environment representation is a crucial component of automated
vehicles. Using single range sensor scans, data is often too sparse and subject
to occlusions. Therefore, we present a method to augment occupancy grid maps
from single views to be similar to evidential occupancy maps acquired from
different views using Deep Learning. To accomplish this, we estimate motion
between subsequent range sensor measurements and create an evidential 3D voxel
map in an extensive post-processing step. Within this voxel map, we explicitly
model uncertainty using evidence theory and create a 2D projection using
combination rules. As input for our neural networks, we use a multi-layer grid
map consisting of the three features detections, transmissions and intensity,
each for ground and non-ground measurements. Finally, we perform a quantitative
and qualitative evaluation which shows that different network architectures
accurately infer evidential measures in real-time.

Robotics