  Understanding driving situations regardless the conditions of the traffic
scene is a cornerstone on the path towards autonomous vehicles; however,
despite common sensor setups already include complementary devices such as
LiDAR or radar, most of the research on perception systems has traditionally
focused on computer vision. We present a LiDAR-based 3D object detection
pipeline entailing three stages. First, laser information is projected into a
novel cell encoding for bird's eye view projection. Later, both object location
on the plane and its heading are estimated through a convolutional neural
network originally designed for image processing. Finally, 3D oriented
detections are computed in a post-processing phase. Experiments on KITTI
dataset show that the proposed framework achieves state-of-the-art results
among comparable methods. Further tests with different LiDAR sensors in real
scenarios assess the multi-device capabilities of the approach.
