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Acta Photonica Sinica, Volume. 50, Issue 11, 1112002(2021)

Research on Point Cloud Registration Method Using Gravity Feature Transformation

Raobo LI1, Xiping YUAN2,3, Shu GAN1,2、*, Rui BI1, Sha GAO1, and Lin HU1
Author Affiliations
  • 1Faculty of Land Resources and Engineering，Kunming University of Science and Technology，Kunming 650093，China
  • 2Application Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province，Kunming 650093，China
  • 3College of Geosciences and Engineering，West Yunnan University of Applied Sciences，Dali，Yunnan 671009，China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
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    References (23)
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    Figures & Tables(10)
    The formation of three eigenvectors

    Fig. 1. The formation of three eigenvectors

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    Schematic diagram of the nearest point constructed by the mean value of coordinate points

    Fig. 2. Schematic diagram of the nearest point constructed by the mean value of coordinate points

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    Rotation does not change the relative distance between the points and the angle of the vector

    Fig. 3. Rotation does not change the relative distance between the points and the angle of the vector

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    Registration results of Bunny

    Fig. 4. Registration results of Bunny

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    Registration results of Horse

    Fig. 5. Registration results of Horse

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    Two sets of physical maps

    Fig. 6. Two sets of physical maps

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    Registration results of Tea caddy

    Fig. 7. Registration results of Tea caddy

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    Registration results of Cup

    Fig. 8. Registration results of Cup

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    • Table 1. Comparison of classic point cloud data registration

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      Table 1. Comparison of classic point cloud data registration

      AlgorithmTime/sMSE/mm
      BunnyHorseBunnyHorse
      Algorithm in Ref.［11］35.1337.920.087 20.085 6
      Algorithm in Ref.［18］49.3551.450.021 40.019 2
      Algorithm in Ref.［19］34.6236.240.015 40.012 0
      Classic ICP42.7245.460.276 30.253 8
      Proposed algorithm24.5326.370.012 60.010 2

    • Table 2. Comparison of registration of measured point cloud data

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      Table 2. Comparison of registration of measured point cloud data

      AlgorithmTime/sMSE/mm
      Tea caddyCupTea caddyCup
      Algorithm in Ref.［11］63.2060.740.095 20.094 9
      Algorithm in Ref.［18］85.4780.290.029 40.028 7
      Algorithm in Ref.［19］62.1859.220.020 30.019 1
      Classic ICP75.4372.170.337 50.302 6
      Proposed algorithm53.8449.190.018 90.017 7
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    Raobo LI, Xiping YUAN, Shu GAN, Rui BI, Sha GAO, Lin HU. Research on Point Cloud Registration Method Using Gravity Feature Transformation[J]. Acta Photonica Sinica, 2021, 50(11): 1112002

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Dec. 29, 2020

    Accepted: Feb. 22, 2021

    Published Online: Dec. 2, 2021

    The Author Email: GAN Shu (n1480@qq.com)

    DOI:10.3788/gzxb20215011.1112002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology