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Laser & Optoelectronics Progress, Volume. 61, Issue 10, 1015003(2024)

Global Registration Method for Laser SLAM Point Clouds Based on Graph Optimization

Hao Tang1,2, Dong Li1,2、*, Cheng Wang2, Sheng Nie2, Jiayin Liu1, and Ye Duan1
Author Affiliations
  • 1Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, Yunnan , China
  • 2Key Lab of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (15)
    Equations (3)
    References (23)
    Cited By (2)
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    Paper Information
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    Figures & Tables(15)
    Loop closure edges in the pose graph

    Fig. 1. Loop closure edges in the pose graph

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    Global registration flow chart

    Fig. 2. Global registration flow chart

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    FAST-LIO 2 algorithm result of Data 1

    Fig. 3. FAST-LIO 2 algorithm result of Data 1

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    FAST-LIO 2 algorithm result of Data 2

    Fig. 4. FAST-LIO 2 algorithm result of Data 2

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    FAST-LIO 2 algorithm result of Data 3

    Fig. 5. FAST-LIO 2 algorithm result of Data 3

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    FAST-LIO 2 algorithm result of Data 4

    Fig. 6. FAST-LIO 2 algorithm result of Data 4

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    Comparison of point clouds before and after trajectory drift error optimization for Data 1 and Data 2

    Fig. 7. Comparison of point clouds before and after trajectory drift error optimization for Data 1 and Data 2

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    Comparison of point clouds before and after trajectory drift error optimization for Data 3 and Data 4

    Fig. 8. Comparison of point clouds before and after trajectory drift error optimization for Data 3 and Data 4

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    Scene comparison of Data 1 before and after optimization

    Fig. 9. Scene comparison of Data 1 before and after optimization

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    Scene comparison of Data 2 before and after optimization

    Fig. 10. Scene comparison of Data 2 before and after optimization

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    Scene comparison of Data 3 before and after optimization

    Fig. 11. Scene comparison of Data 3 before and after optimization

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    Scene comparison of Data 4 before and after optimization

    Fig. 12. Scene comparison of Data 4 before and after optimization

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    • Table 1. Description of input data

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      Table 1. Description of input data

      Data nameTrajectory length /mPoint cloud frame rateScanning time /s
      Data 14102018797
      Data 229302700265
      Data 335803420344
      Data 420103180322

    • Table 2. Description of experimental results

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      Table 2. Description of experimental results

      Data

      name

      Processing

      time /s

      Number of coarse

      optimization iterations

      Number of fine

      optimization iterations

      Data 174828
      Data 2100045
      Data 3123135
      Data 4117335

    • Table 3. Average value of the distance RMSE of the matching point pairs

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      Table 3. Average value of the distance RMSE of the matching point pairs

      Optimization stateData 1Data 2Data 3Data 4
      Before optimization2.66710.34819.0183.412
      Drift error optimization0.2310.3290.3980.223
      After coarse optimization0.1710.2840.3420.193
      After fine optimization0.1580.2110.2180.157
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    Hao Tang, Dong Li, Cheng Wang, Sheng Nie, Jiayin Liu, Ye Duan. Global Registration Method for Laser SLAM Point Clouds Based on Graph Optimization[J]. Laser & Optoelectronics Progress, 2024, 61(10): 1015003

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    Paper Information

    Category: Machine Vision

    Received: Aug. 28, 2023

    Accepted: Oct. 20, 2023

    Published Online: Apr. 29, 2024

    The Author Email: Dong Li (lidong@aircas.ac.cn)

    DOI:10.3788/LOP232000

    CSTR:32186.14.LOP232000

    Topics

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