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3D object recognition method with multiple feature extraction from LiDAR point clouds

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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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Acknowledgements

This research was supported by National Natural Science Foundation of China (61503005), Beijing Natural Science Foundation (4184086), Beijing Young Topnotch Talents Cultivation Program (No. CIT&TCD201904009), the Great Wall Scholar Program (CIT&TCD20190304), NCUT “The Belt and Road” Talent Training Base Project, and NCUT “Yuyou” Project.

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Authors and Affiliations

  1. North China University of Technology, No. 5 Jinyuanzhuang Road, Shijingshan District, Beijing, 100-144, China

    Yifei Tian, Wei Song, Su Sun & Shuanghui Zou

  2. Department of Computer and Information Science, University of Macau, Taipa, 999-078, Macau, China

    Yifei Tian & Simon Fong

  3. Beijing Key Lab on Urban Intelligent Traffic Control Technology, Beijing, 100-144, China

    Wei Song

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  1. Yifei Tian
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  2. Wei Song
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  3. Su Sun
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  4. Simon Fong
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  5. Shuanghui Zou
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Correspondence to Wei Song.

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Tian, Y., Song, W., Sun, S. et al. 3D object recognition method with multiple feature extraction from LiDAR point clouds. J Supercomput 75, 4430–4442 (2019). https://doi.org/10.1007/s11227-019-02830-9

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  • DOI: https://doi.org/10.1007/s11227-019-02830-9

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