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The Journal of Supercomputing

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30-03-2019

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

Authors: Yifei Tian, Wei Song, Su Sun, Simon Fong, Shuanghui Zou

Published in: The Journal of Supercomputing | Issue 8/2019

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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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Title: 3D object recognition method with multiple feature extraction from LiDAR point clouds
Authors: Yifei Tian
Wei Song
Su Sun
Simon Fong
Shuanghui Zou
Publication date: 30-03-2019
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 8/2019
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-019-02830-9

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