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Laser & Optoelectronics Progress, Volume. 60, Issue 24, 2428003(2023)

LiDAR Point Cloud Correction and Location Based on Multisensor Fusion

Wenhao Pu1,2, Xixiang Liu1,2、*, Hao Chen1,3, Hao Xu1,2, and Ye Liu1,2
Author Affiliations
  • 1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, Jiangsu, China
  • 2Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, Jiangsu, China
  • 3Nanjing Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, Jiangsu, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
    Equations (15)
    References (18)
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    Figures & Tables(11)
    Schematic of LiDAR point cloud distortion generation. (a) LiDAR scanning at initial time; (b) LiDAR scanning at motion state; (c) point cloud coordinates with distortion

    Fig. 1. Schematic of LiDAR point cloud distortion generation. (a) LiDAR scanning at initial time; (b) LiDAR scanning at motion state; (c) point cloud coordinates with distortion

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    Schematic of algorithm flow

    Fig. 2. Schematic of algorithm flow

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    Schematic of correction results of LiDAR point cloud distortion. (a) Diagram of correction of x-direction coordinates; (b) diagram of correction of y-direction coordinates

    Fig. 3. Schematic of correction results of LiDAR point cloud distortion. (a) Diagram of correction of x-direction coordinates; (b) diagram of correction of y-direction coordinates

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    Point cloud views of different point cloud distortion correction methods in corridor environment. (a) Point cloud view without distortion compensation; (b) LOAM method; (c) F-LOAM method; (d) proposed method

    Fig. 4. Point cloud views of different point cloud distortion correction methods in corridor environment. (a) Point cloud view without distortion compensation; (b) LOAM method; (c) F-LOAM method; (d) proposed method

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    Point cloud views of different point cloud distortion correction methods in building environment. (a) Point cloud view without distortion compensation; (b) LOAM method; (c) F-LOAM method; (d) proposed method

    Fig. 5. Point cloud views of different point cloud distortion correction methods in building environment. (a) Point cloud view without distortion compensation; (b) LOAM method; (c) F-LOAM method; (d) proposed method

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    Trajectory comparison between different algorithms

    Fig. 6. Trajectory comparison between different algorithms

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    Comparison of errors in three axes. (a) Along the x axis; (b) along the y axis; (c) along the z axis

    Fig. 7. Comparison of errors in three axes. (a) Along the x axis; (b) along the y axis; (c) along the z axis

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    Comparison of trajectory between algorithms in the actual scene

    Fig. 8. Comparison of trajectory between algorithms in the actual scene

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    • Table 1. Comparison of error results in two directions of the three algorithms

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      Table 1. Comparison of error results in two directions of the three algorithms

      AlgorithmAverage error alongx-direction /mAverage error alongy-direction /m
      LOAM0.2810.213
      F-LOAM0.2210.152
      Proposed algorithm0.1890.128

    • Table 2. Comparison of positioning results of three algorithms on KITTI dataset

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      Table 2. Comparison of positioning results of three algorithms on KITTI dataset

      AlgorithmAverage error /mRMSE /mError sum of squares /m2Standard deviation /mAverage iteration time /ms
      LOAM17.86119.2524 769 827.82211.5170.519
      F-LOAM1.7284.6342 254.8074.2300.491
      Proposed algorithm1.0033.636138.7933.0990.481

    • Table 3. Comparison of positioning results of the three algorithms in actual scene

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      Table 3. Comparison of positioning results of the three algorithms in actual scene

      AlgorithmAverage error /mRMSE /mError sum of squares /m2Standard deviation /mAverage iteration time /ms
      LOAM3.5620.851491.3660.9360.604
      F-LOAM1.8870.531414.8350.2820.589
      Proposed algorithm1.1020.402122.2330.0250.575
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    Wenhao Pu, Xixiang Liu, Hao Chen, Hao Xu, Ye Liu. LiDAR Point Cloud Correction and Location Based on Multisensor Fusion[J]. Laser & Optoelectronics Progress, 2023, 60(24): 2428003

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Mar. 2, 2023

    Accepted: Apr. 7, 2023

    Published Online: Dec. 4, 2023

    The Author Email: Liu Xixiang (scliuseu@163.com)

    DOI:10.3788/LOP230762

    Topics

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