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Chinese Journal of Lasers, Volume. 48, Issue 24, 2410001(2021)

Position Detection Algorithm of Road Obstacles Based on 3D LiDAR

Jie Hu1,2,3、*, Han Liu1,2,3, Wencai Xu1,2,3, and Liang Zhao1,2,3
Author Affiliations
  • 1Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, Hubei 430070, China
  • 2Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, Hubei 430070, China
  • 3Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan, Hubei 430070, China;
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (25)
    Cited By (11)
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    Figures & Tables(13)
    Flow chart of objection position detection algorithm

    Fig. 1. Flow chart of objection position detection algorithm

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    Sector bins distribution and polar coordinate system

    Fig. 2. Sector bins distribution and polar coordinate system

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    Pseudo-code block diagram of ground segmentation algorithm

    Fig. 3. Pseudo-code block diagram of ground segmentation algorithm

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    Schematic diagram of point cloud projected by LiDAR

    Fig. 4. Schematic diagram of point cloud projected by LiDAR

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    Comparison of point clouds from different perspectives. (a) 3D view; (b) XOY plane view

    Fig. 5. Comparison of point clouds from different perspectives. (a) 3D view; (b) XOY plane view

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    Object direction angle detection

    Fig. 6. Object direction angle detection

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    Coordinate system rotation process

    Fig. 7. Coordinate system rotation process

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    Pseudo code block diagram of direction and size detection algorithm

    Fig. 8. Pseudo code block diagram of direction and size detection algorithm

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    Installation drawing of vehicle sensor

    Fig. 9. Installation drawing of vehicle sensor

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    Detection performance of the algorithm. (a) Velodyne VLP16; (b) LEISHEN C32151L; (c) ZVISION ML-30S

    Fig. 10. Detection performance of the algorithm. (a) Velodyne VLP16; (b) LEISHEN C32151L; (c) ZVISION ML-30S

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    Detection time of different LiDARs. (a) Velodyne VLP16; (b) LEISHEN C32151L; (c) ZVISION ML-30S

    Fig. 11. Detection time of different LiDARs. (a) Velodyne VLP16; (b) LEISHEN C32151L; (c) ZVISION ML-30S

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    • Table 1. Performance parameters of LiDAR

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      Table 1. Performance parameters of LiDAR

      Performance parametersVelodyne VLP16LEISHEN C32151LZVISION ML-30S
      Number of lines1632140
      Measuring range /m10015030
      Measuring accuracy /cm±3±2±3
      Vertical angle range /(°)303070
      Vertical angular resolution /(°)211
      Horizontal angle range /(°)360360150
      Horizontal angular resolution /(°)0.20.50.3
      Scanning frequency /Hz101010

    • Table 2. Algorithm effect evaluation table unit: %

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      Table 2. Algorithm effect evaluation table unit: %

      LiDARAlgorithmTPAFNATTPAPPA
      Velodyne VLP16MBR81.245.3869.5684.44
      TPE81.245.3868.7683.13
      RDME81.245.3870.0385.56
      LEISHEN C32151LMBR81.806.8470.8386.93
      TPE81.806.8471.3487.28
      RDME81.806.8474.2390.91
      ZVISION ML-30SMBR80.375.5669.1686.05
      TPE80.375.5670.1787.62
      RDME80.375.5673.6894.19
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    Jie Hu, Han Liu, Wencai Xu, Liang Zhao. Position Detection Algorithm of Road Obstacles Based on 3D LiDAR[J]. Chinese Journal of Lasers, 2021, 48(24): 2410001

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    Paper Information

    Received: Apr. 15, 2021

    Accepted: May. 17, 2021

    Published Online: Nov. 25, 2021

    The Author Email: Hu Jie (auto_hj@163.com)

    DOI:10.3788/CJL202148.2410001

    Topics

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