Vol.:(0123456789) The Journal of Supercomputing https://doi.org/10.1007/s11227-019-02830-9 1 3 3D object recognition method with multiple feature extraction from LiDAR point clouds Yifei Tian 1,2  · Wei Song 1,3  · Su Sun 1  · Simon Fong 2  · Shuanghui Zou 1 © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract During autonomous driving, fast and accurate object recognition supports envi- ronment perception for local path planning of unmanned ground vehicles. Feature extraction and object recognition from large-scale 3D point clouds incur massive computational and time costs. To implement fast environment perception, this paper proposes a 3D recognition system with multiple feature extraction from light detec- tion and ranging point clouds modifed by parallel computing. Efective object feature extraction is a necessary step prior to executing an object recognition pro- cedure. In the proposed system, multiple geometry features of a point cloud that resides in corresponding voxels are computed concurrently. In addition, a scale flter is employed to convert feature vectors from uncertain count voxels to a normalized object feature matrix, which is convenient for object-recognizing classifers. After generating the object feature matrices of all voxels, an initialized multilayer neural network (NN) model is trained ofine through a large number of iterations. Using the trained NN model, real-time object recognition is realized using parallel com- puting technology to accelerate computation. Keywords 3D object recognition · Feature extraction · LiDAR point cloud · Parallel computing * Wei Song sw@ncut.edu.cn 1 North China University of Technology, No. 5 Jinyuanzhuang Road, Shijingshan District, Beijing 100-144, China 2 Department of Computer and Information Science, University of Macau, Taipa 999-078, Macau, China 3 Beijing Key Lab on Urban Intelligent Trafc Control Technology, Beijing 100-144, China