BirdNet: A 3D Object Detection Framework from LiDAR Information

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 f...

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Bibliographic Details
Published in:Proceedings (IEEE Conference on Intelligent Transportation Systems) pp. 3517 - 3523
Main Authors: Beltran, Jorge, Guindel, Carlos, Moreno, Francisco Miguel, Cruzado, Daniel, Garcia, Fernando, De La Escalera, Arturo
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2018
Subjects:
Feature extraction
Laser beams
Laser radar
Object detection
Task analysis
Three-dimensional displays
Two dimensional displays
ISBN:9781728103211, 1728103215
ISSN:2153-0017
Online Access:Get full text
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Summary: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.
ISBN:9781728103211
1728103215
ISSN:2153-0017
DOI:10.1109/ITSC.2018.8569311