Laser & Optoelectronics Progress, Volume. 62, Issue 2, 0215007(2025)

A Novel Three-Dimensional Point Cloud Matching Algorithm Based on Point Region Features and Weighted Voting

Junjun Lu1、*, Ke Ding2, Zuoxi Zhao1, and Feng Wang2
Author Affiliations
  • 1College of Engineering, South China Agricultural University, Guangzhou 510640, Guangdong , China
  • 2Guangdong University of Technology, Guangzhou 510006, Guangdong , China
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    References(30)

    [14] Chang Y H, Chen N S, Rao L et al. Lidar point cloud descriptor with rotation and translation invariance in dynamic environment[J]. Acta Optica Sinica, 42, 2401007(2022).

    [22] Zhou L, Zhao B, Liang D et al. LDASH: a local feature descriptor of point cloud with high discrimination and strong robustness[J]. Laser & Optoelectronics Progress, 61, 1215007(2024).

    [26] Yu H S, Fu Q, Sun J et al. Improved 3D-NDT point cloud registration algorithm for indoor mobile robot[J]. Chinese Journal of Scientific Instrument, 40, 151-161(2019).

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    Junjun Lu, Ke Ding, Zuoxi Zhao, Feng Wang. A Novel Three-Dimensional Point Cloud Matching Algorithm Based on Point Region Features and Weighted Voting[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0215007

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    Paper Information

    Category: Machine Vision

    Received: Apr. 8, 2024

    Accepted: Jun. 12, 2024

    Published Online: Jan. 6, 2025

    The Author Email:

    DOI:10.3788/LOP241055

    CSTR:32186.14.LOP241055

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