Affiliated with RCfalse
Classifying 3D objects in LiDAR point clouds with a back-propagation neural network
Wei Song1; Shuanghui Zou1; Yifei Tian2; Simon Fong2; Kyungeun Cho3

Due to object recognition accuracy limitations, unmanned ground vehicles (UGVs) must perceive their environments for local path planning and object avoidance. To gather high-precision information about the UGV's surroundings, Light Detection and Ranging (LiDAR) is frequently used to collect large-scale point clouds. However, the complex spatial features of these clouds, such as being unstructured, diffuse, and disordered, make it difficult to segment and recognize individual objects. This paper therefore develops an object feature extraction and classification system that uses LiDAR point clouds to classify 3D objects in urban environments. After eliminating the ground points via a height threshold method, this describes the 3D objects in terms of their geometrical features, namely their volume, density, and eigenvalues. A back-propagation neural network (BPNN) model is trained (over the course of many iterations) to use these extracted features to classify objects into five types. During the training period, the parameters in each layer of the BPNN model are continually changed and modified via back-propagation using a non-linear sigmoid function. In the system, the object segmentation process supports obstacle detection for autonomous driving, and the object recognition method provides an environment perception function for terrain modeling. Our experimental results indicate that the object recognition accuracy achieve 91.5% in outdoor environment.

Keyword3d Object Recognition Back-propagation Neural Network Feature Extraction Lidar Point Cloud
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000447286200001
Fulltext Access
Citation statistics
Cited Times [WOS]:23   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Corresponding AuthorWei Song
Affiliation1.North China University of Technology, Beijing, China
2.Dept. Computer and Information Science, University of Macau, Macau, China
3.Dept. Multimedia Engineering, Dongguk University, Seoul, South Korea
Recommended Citation
GB/T 7714
Wei Song,Shuanghui Zou,Yifei Tian,et al. Classifying 3D objects in LiDAR point clouds with a back-propagation neural network[J]. HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES,2018,8.
APA Wei Song,Shuanghui Zou,Yifei Tian,Simon Fong,&Kyungeun Cho.(2018).Classifying 3D objects in LiDAR point clouds with a back-propagation neural network.HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES,8.
MLA Wei Song,et al."Classifying 3D objects in LiDAR point clouds with a back-propagation neural network".HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 8(2018).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wei Song]'s Articles
[Shuanghui Zou]'s Articles
[Yifei Tian]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wei Song]'s Articles
[Shuanghui Zou]'s Articles
[Yifei Tian]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wei Song]'s Articles
[Shuanghui Zou]'s Articles
[Yifei Tian]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.