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Laser & Optoelectronics Progress, Volume. 57, Issue 12, 121503(2020)

Point Cloud Registration Algorithm Based on Cosine Similarity

Xu Zhan1,2 and Yong Cai1、*
Author Affiliations
  • 1School of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China
  • 2School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China;
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    AbstractGet PDF(in Chinese)
    Figures&Tables (16)
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    References (14)
    Cited By (3)
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    Figures & Tables(16)
    Point cloud rasterization.(a) Original point cloud rasterization; (b) target point cloud rasterization

    Fig. 1. Point cloud rasterization.(a) Original point cloud rasterization; (b) target point cloud rasterization

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    Search for optimal R flowchart based on DE

    Fig. 2. Search for optimal R flowchart based on DE

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    Comparison of registration results of various algorithms in Feet perspective 1. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 3. Comparison of registration results of various algorithms in Feet perspective 1. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    Comparison of registration results of various algorithms in Feet perspective 2. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 4. Comparison of registration results of various algorithms in Feet perspective 2. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    Comparison of registration results of various algorithms in Cow perspective 1. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 5. Comparison of registration results of various algorithms in Cow perspective 1. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    Comparison of registration results of various algorithms in Cow perspective 2. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 6. Comparison of registration results of various algorithms in Cow perspective 2. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    Comparison of registration results of various algorithms for adding noise to Feet. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 7. Comparison of registration results of various algorithms for adding noise to Feet. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    Comparison of registration results of various algorithms for adding noise to Cow. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 8. Comparison of registration results of various algorithms for adding noise to Cow. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    Comparison of registration results of various algorithms for Feet losing 50% data. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 9. Comparison of registration results of various algorithms for Feet losing 50% data. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    Comparison of registration results of various algorithms for Feet losing 75% data. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 10. Comparison of registration results of various algorithms for Feet losing 75% data. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    Comparison of registration results of various algorithms for Cow losing 50% data. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 11. Comparison of registration results of various algorithms for Cow losing 50% data. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    Comparison of registration results of various algorithms for Cow losing 75% data. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

    Fig. 12. Comparison of registration results of various algorithms for Cow losing 75% data. (a) Unregistered results; (b) CPD algorithm; (c) Scale-ICP algorithm; (d) Go-ICP algorithm; (e) proposed algorithm

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    • Table 1. Comparison of MSE results of different models from different views

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      Table 1. Comparison of MSE results of different models from different views

      ModelMSE
      ViewCPDScale-ICPGo-ICPProposed
      Feet12.67×10-56.79×10-41.62×10-94.90×10-9
      21.00×10-36.79×10-48.34×10-41.45×10-7
      Cow19.21×10-52.83×10-318.01×10-102.87×10-7
      21.30×10-31.50×10-31.60×10-34.82×10-8

    • Table 2. Comparison of running time of different models from different views

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      Table 2. Comparison of running time of different models from different views

      ModelRunning time/s
      ViewCPDScale-ICPGo-ICPProposed
      Feet12.510.9024.8817.96
      23.370.8924.7517.77
      Cow13.000.9224.5326.13
      23.051.2725.4925.69

    • Table 3. Comparison of MSE results of interference signal added by different models

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      Table 3. Comparison of MSE results of interference signal added by different models

      ModelMSE
      CPDScale-ICPGo-ICPProposed
      Feet3.86×10-57.02×10-42.21×10-54.84×10-5
      Cow1.02×10-41.50×10-32.61×10-56.81×10-5

    • Table 4. Comparison of MSE results of different models with different sizes of missing data

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      Table 4. Comparison of MSE results of different models with different sizes of missing data

      ModelMSE
      Missing /%CPDScale-ICPGo-ICPProposed
      Feet503.29×10-56.60×10-31.69×10-71.25×10-5
      753.17×10-52.40×10-21.67×10-97.16×10-7
      Cow502.40×10-41.89×10-21.90×10-31.48×10-4
      759.31×10-42.84×10-11.80×10-32.26×10-4
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    Xu Zhan, Yong Cai. Point Cloud Registration Algorithm Based on Cosine Similarity[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121503

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    Paper Information

    Category: Machine Vision

    Received: Sep. 2, 2019

    Accepted: Nov. 8, 2019

    Published Online: Jun. 3, 2020

    The Author Email: Cai Yong (caiy@swust.edu)

    DOI:10.3788/LOP57.121503

    Topics

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