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Laser & Optoelectronics Progress, Volume. 59, Issue 22, 2210007(2022)

Obstacle Detection for a Pipeline Point Cloud Based on Time Series and Neighborhood Analysis

Shiyu Lin1,3, Xuejiao Yan2, Zhe Xie2, Hongwen Fu2, Song Jiang2, Hongzhi Jiang1,3, Xudong Li1,3、*, and Huijie Zhao1,3、**
Author Affiliations
  • 1Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
  • 2Shanghai Aerospace System Research Institute, Shanghai 201108, China
  • 3Qingdao Research Institute of Beihang University, Qingdao 266100, Shandong , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
    Equations (12)
    References (18)
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    Figures & Tables(10)
    Flow chart of obstacle detection

    Fig. 1. Flow chart of obstacle detection

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    Schematic diagram of detection area

    Fig. 2. Schematic diagram of detection area

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    Schematic diagram of denoising based on time series and neighborhood analysis

    Fig. 3. Schematic diagram of denoising based on time series and neighborhood analysis

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    Filtering effect diagrams when n taking different values. (a) Original images; (b) n=2; (c) n=1; (d) n=0; (e) n=-1

    Fig. 4. Filtering effect diagrams when n taking different values. (a) Original images; (b) n=2; (c) n=1; (d) n=0; (e) n=-1

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    Pipeline physical map and point cloud map. (a) Pipeline physical map; (b) point cloud map

    Fig. 5. Pipeline physical map and point cloud map. (a) Pipeline physical map; (b) point cloud map

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    Point cloud after preprocess and point cloud after orientation adjustment. (a) Single frame point cloud after preprocessing (50907 points); (b) point cloud after fusing 5 frames (255784 points)

    Fig. 6. Point cloud after preprocess and point cloud after orientation adjustment. (a) Single frame point cloud after preprocessing (50907 points); (b) point cloud after fusing 5 frames (255784 points)

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    Comparison of point cloud distribution before and after denoising. (a) Distance distribution from point to axis before denoising; (b) distance distribution from point to axis after denoising

    Fig. 7. Comparison of point cloud distribution before and after denoising. (a) Distance distribution from point to axis before denoising; (b) distance distribution from point to axis after denoising

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    Images of different filtering methods. (a) Original images; (b) image processed by Gaussian filtering; (c) image processed by proposed algorithm

    Fig. 8. Images of different filtering methods. (a) Original images; (b) image processed by Gaussian filtering; (c) image processed by proposed algorithm

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    • Table 1. Noise comparison table before and after denoising

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      Table 1. Noise comparison table before and after denoising

      ParameterBefore denoisingAfter fusion & denoising
      Number of points5090780727
      Noise （mean） /mm4.32.6
      Noise （standard deviation） /mm1.91.2
      Noise （max） /mm12.77.4
      Number of noise greater than 10 mm2790

    • Table 2. Result of pipeline obstacle detection

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      Table 2. Result of pipeline obstacle detection

      MethodObstacle size /mmR /%P /%Average time /s
      Direct detection9.598.859.80.12
      9.910061.4
      10.299.960.8
      Detection by Gauss filtering9.579.479.80.17
      9.988.890.4
      10.290.890.4
      Detection by proposed method9.595.098.40.72
      9.999.498.0
      10.299.498.0
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    Shiyu Lin, Xuejiao Yan, Zhe Xie, Hongwen Fu, Song Jiang, Hongzhi Jiang, Xudong Li, Huijie Zhao. Obstacle Detection for a Pipeline Point Cloud Based on Time Series and Neighborhood Analysis[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2210007

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    Paper Information

    Category: Image Processing

    Received: Aug. 18, 2021

    Accepted: Oct. 13, 2021

    Published Online: Sep. 23, 2022

    The Author Email: Xudong Li (xdli@buaa.edu.cn), Huijie Zhao (hjzhao@buaa.edu.cn)

    DOI:10.3788/LOP202259.2210007

    Topics

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