Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment

This paper presents an object classification method for vision and light detection and ranging (LIDAR) fusion of autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image upsampling theory. By creating a point cloud of LIDAR data upsampling and conv...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial informatics 2018-09, Vol.14 (9), p.4224-4231
Main Authors: Gao, Hongbo, Cheng, Bo, Wang, Jianqiang, Li, Keqiang, Zhao, Jianhui, Li, Deyi
Format: Article
Language:English
Subjects:
Artificial neural networks
Autonomous vehicle
Autonomous vehicles
Cameras
Classification
convolutional neural network (CNN)
Informatics
Laser radar
Lidar
Neural networks
object classification
Repair & maintenance
sensor fusion
Three-dimensional displays
Uncertainty
Vision
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents an object classification method for vision and light detection and ranging (LIDAR) fusion of autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image upsampling theory. By creating a point cloud of LIDAR data upsampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is also adopted to guarantee both object classification accuracy and minimal loss. Experimental results are presented and show the effectiveness and efficiency of object classification strategies.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2018.2822828