  A detailed environment perception is a crucial component of automated
vehicles. However, to deal with the amount of perceived information, we also
require segmentation strategies. Based on a grid map environment
representation, well-suited for sensor fusion, free-space estimation and
machine learning, we detect and classify objects using deep convolutional
neural networks. As input for our networks we use a multi-layer grid map
efficiently encoding 3D range sensor information. The inference output consists
of a list of rotated bounding boxes with associated semantic classes. We
conduct extensive ablation studies, highlight important design considerations
when using grid maps and evaluate our models on the KITTI Bird's Eye View
benchmark. Qualitative and quantitative benchmark results show that we achieve
robust detection and state of the art accuracy solely using top-view grid maps
from range sensor data.
