Classifying 3D objects in LiDAR point clouds with a back-propagation neural network

• Published in: Human-centric computing and information sciences 2018-10, Vol.8 (1), p.1-12, Article 29
• Main Authors: Song, Wei, Zou, Shuanghui, Tian, Yifei, Fong, Simon, Cho, Kyungeun
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Artificial neural networks; Back propagation; Back propagation networks; Classification; Communications Engineering; Computer Science; Computer Systems Organization and Communication Networks; Eigenvalues; Feature extraction; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Lidar; Networks; Neural networks; Object recognition; Obstacle avoidance; Path planning; Propagation; Three dimensional models; Unmanned ground vehicles; Urban environments; User Interfaces and Human Computer Interaction
• ISSN: 2192-1962; 2192-1962
• DOI: 10.1186/s13673-018-0152-7

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.