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Registration of 3D Point Clouds Based on Voxelization Simplify and Accelerated Iterative Closest Point Algorithm

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Artificial Intelligence (CICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13069))

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Abstract

3D point clouds have lots of applications in the fields of reverse engineering, laser remote sensing and automatic driving. The registration of point clouds scanned form different positions or different angles is the basis for shape understanding and analysis. However, due to the complex environment and the large amount of data, the automatic registration of different scans is still a challenging problem. In this work, we propose a fast registration algorithm using the prior information under a voxel structure. In this algorithm, the point cloud is firstly voxelized and organized with a 3D voxel structure. Then, we take an initial alignment based on reliable parts according to prior information. Finally we refine registration using kd-tree to accelerate Iterative Closest Point algorithm. To evaluate our algorithm, we take both synthetic data and real data experiments. The results show that our algorithm is higher efficiency and robustness.

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The work was supported by the National College Students innovation and entrepreneurship training program No. G201910022067.

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Authors and Affiliations

  1. College of Science, Beijing Forestry University, Beijing, 100083, China

    Jiayu Wang & Hongjun Li

Authors
  1. Jiayu Wang
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  2. Hongjun Li
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Corresponding author

Correspondence to Hongjun Li .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Yiran Chen

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. University of British Columbia, Vancouver, BC, Canada

    Jane Wang

  5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

  6. Xidian University, Xi’an, China

    Weisheng Dong

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Wang, J., Li, H. (2021). Registration of 3D Point Clouds Based on Voxelization Simplify and Accelerated Iterative Closest Point Algorithm. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_24

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  • DOI: https://doi.org/10.1007/978-3-030-93046-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93045-5

  • Online ISBN: 978-3-030-93046-2

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