  In this paper, we consider the transformation of laser range measurements
into a top-view grid map representation to approach the task of LiDAR-only
semantic segmentation. Since the recent publication of the SemanticKITTI data
set, researchers are now able to study semantic segmentation of urban LiDAR
sequences based on a reasonable amount of data. While other approaches propose
to directly learn on the 3D point clouds, we are exploiting a grid map
framework to extract relevant information and represent them by using
multi-layer grid maps. This representation allows us to use well-studied deep
learning architectures from the image domain to predict a dense semantic grid
map using only the sparse input data of a single LiDAR scan. We compare
single-layer and multi-layer approaches and demonstrate the benefit of a
multi-layer grid map input. Since the grid map representation allows us to
predict a dense, 360{\deg} semantic environment representation, we further
develop a method to combine the semantic information from multiple scans and
create dense ground truth grids. This method allows us to evaluate and compare
the performance of our models not only based on grid cells with a detection,
but on the full visible measurement range.

Computer Vision and Pattern Recognition;Robotics;Image and Video Processing