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.
@article{arxiv.1805.01195,
title = {BirdNet: a 3D Object Detection Framework from LiDAR information},
author = {Jorge Beltran and Carlos Guindel and Francisco Miguel Moreno and Daniel Cruzado and Fernando Garcia and Arturo de la Escalera},
journal= {arXiv preprint arXiv:1805.01195},
year = {2018}
}
Comments
Submittied to IEEE International Conference on Intelligent Transportation Systems 2018 (ITSC)