%0 Conference Paper %1 8569311 %A Beltrán, J. %A Guindel, C. %A Moreno, F. M. %A Cruzado, D. %A García, F. %A De La Escalera, A. %B 2018 21st International Conference on Intelligent Transportation Systems (ITSC) %D 2018 %K dnn lidar order1 pedestrian %P 3517-3523 %R 10.1109/ITSC.2018.8569311 %T BirdNet: A 3D Object Detection Framework from LiDAR Information %U https://ieeexplore.ieee.org/document/8569311 %X 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.

@inproceedings{8569311, abstract = {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.}, added-at = {2020-05-27T23:32:33.000+0200}, author = {{Beltrán}, J. and {Guindel}, C. and {Moreno}, F. M. and {Cruzado}, D. and {García}, F. and {De La Escalera}, A.}, biburl = {https://www.bibsonomy.org/bibtex/273207ac2bb375f438bce8569b5a3b32d/sohnki}, booktitle = {2018 21st International Conference on Intelligent Transportation Systems (ITSC)}, description = {BirdNet: A 3D Object Detection Framework from LiDAR Information - IEEE Conference Publication}, doi = {10.1109/ITSC.2018.8569311}, interhash = {037961dd783c6d1c30b0f5a83de26a5f}, intrahash = {73207ac2bb375f438bce8569b5a3b32d}, issn = {2153-0017}, keywords = {dnn lidar order1 pedestrian}, month = nov, pages = {3517-3523}, timestamp = {2020-06-02T20:02:18.000+0200}, title = {BirdNet: A 3D Object Detection Framework from LiDAR Information}, url = {https://ieeexplore.ieee.org/document/8569311}, year = 2018 }