3D object recognition method with multiple feature extraction from LiDAR point clouds |
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Authors: | Tian Yifei Song Wei Sun Su Fong Simon Zou Shuanghui |
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Affiliation: | 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 Traffic Control Technology, Beijing, 100-144, China ; |
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Abstract: | During autonomous driving, fast and accurate object recognition supports environment 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 detection and ranging point clouds modified by parallel computing. Effective object feature extraction is a necessary step prior to executing an object recognition procedure. In the proposed system, multiple geometry features of a point cloud that resides in corresponding voxels are computed concurrently. In addition, a scale filter is employed to convert feature vectors from uncertain count voxels to a normalized object feature matrix, which is convenient for object-recognizing classifiers. After generating the object feature matrices of all voxels, an initialized multilayer neural network (NN) model is trained offline through a large number of iterations. Using the trained NN model, real-time object recognition is realized using parallel computing technology to accelerate computation.
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