UM
Classifying 3D objects in LiDAR point clouds with a back-propagation neural network
Song, Wei1; Zou, Shuanghui1; Tian, Yifei2; Fong, Simon2; Cho, Kyungeun3
2018-10-12
Source PublicationHUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
ISSN2192-1962
Volume8
AbstractDue 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
DOI10.1186/s13673-018-0152-7
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000447286200001
PublisherSPRINGEROPEN
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Cited Times [WOS]:5   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.North China Univ Technol, Beijing, Peoples R China;
2.Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China;
3.Dongguk Univ, Dept Multimedia Engn, Seoul, South Korea
Recommended Citation
GB/T 7714
Song, Wei,Zou, Shuanghui,Tian, Yifei,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 Song, Wei,Zou, Shuanghui,Tian, Yifei,Fong, Simon,&Cho, Kyungeun.(2018).Classifying 3D objects in LiDAR point clouds with a back-propagation neural network.HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES,8.
MLA Song, Wei,et al."Classifying 3D objects in LiDAR point clouds with a back-propagation neural network".HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 8(2018).
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