Laser & Optoelectronics Progress, Volume. 59, Issue 16, 1628004(2022)

Point Cloud Filtering Algorithm Based on Density Clustering

Guo Tang, Xingsheng Deng*, and Qingyang Wang
Author Affiliations
  • School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, Hunan , China
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    Figures & Tables(45)
    Schematic diagram of DBSCAN
    Flow chart of point cloud filtering algorithm based on density clustering
    Simulation map of laser point cloud position in large survey area
    3D view of S53
    Elevation map of S53
    Filtering result map of S53
    Error point location map of S53
    Relief image after filtering of S53
    Section plane comparison diagram of S53
    Schematic diagram of classification error
    3D view of S61
    Elevation map of S61
    Filtering result map of S61
    Error point location map of S61
    Relief image after filtering of S61
    Section plane comparison diagram of S61
    3D view of S21
    Filtering result map of S21
    Relief image after filtering of S21
    Error point location map of S21
    Mutation point location map of S21
    Re-division result map of S21
    Relief image before filtering of S24
    Elevation map of S24
    Filtering result map of S24
    Error point location map of S24
    Relief image after filtering of S24
    Section plane comparison diagram of S24
    3D view of S71
    Filtering result map of S71
    Relief image after filtering of S71
    Error point location map of S71
    Mutation point location map of S71
    Re-division result map of S71
    • Table 1. Definition of filtering error

      View table

      Table 1. Definition of filtering error

      Reference pointFiltered pointQuantitative evaluation index
      Ground pointsNon-ground pointsType Ⅰ(TⅠ)Type Ⅱ(TⅡ)Total(TE)
      Ground pointsabb/(a+bc/(c+db+c)/(a+b+c+d
      Non-ground pointscd
    • Table 2. Filter error statistics of S53

      View table

      Table 2. Filter error statistics of S53

      SampleTⅠTⅡTE
      S5315.4024.9115.78
    • Table 3. Comparison of S53 total filtering error with other filtering algorithms

      View table

      Table 3. Comparison of S53 total filtering error with other filtering algorithms

      SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
      S5348.4520.198.9112.6052.8117.2927.2437.0728.0715.78
    • Table 4. Filter error statistics of S61

      View table

      Table 4. Filter error statistics of S61

      SampleTⅠTⅡTE
      S615.5615.845.91
    • Table 5. Comparison of S61 total filtering error with other filtering algorithms

      View table

      Table 5. Comparison of S61 total filtering error with other filtering algorithms

      SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
      S6135.872.992.086.9121.6818.9913.4721.6315.455.91
    • Table 6. Filter error statistics of S21

      View table

      Table 6. Filter error statistics of S21

      SampleTⅠTⅡTE
      S210.4311.722.93
    • Table 7. Comparison of S21 total filtering error with other filtering algorithms

      View table

      Table 7. Comparison of S21 total filtering error with other filtering algorithms

      SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
      S218.538.804.252.579.309.844.557.766.952.93
    • Table 8. Filter error statistics of S24

      View table

      Table 8. Filter error statistics of S24

      SampleTⅠTⅡTE
      S2410.4914.9211.71
    • Table 9. Comparison of S24 total filtering error with other filtering algorithms

      View table

      Table 9. Comparison of S24 total filtering error with other filtering algorithms

      SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
      S2413.8313.334.428.6436.0623.2511.5325.2817.0411.71
    • Table 10. Filter error statistics of S71

      View table

      Table 10. Filter error statistics of S71

      SampleTⅠTⅡTE
      S7115.2610.1714.68
    • Table 11. Comparison of S53 total filtering error with other filtering algorithms

      View table

      Table 11. Comparison of S53 total filtering error with other filtering algorithms

      SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
      S7134.222.201.638.8534.985.1116.9721.8315.7214.68
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    Guo Tang, Xingsheng Deng, Qingyang Wang. Point Cloud Filtering Algorithm Based on Density Clustering[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1628004

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: May. 12, 2021

    Accepted: Jul. 13, 2021

    Published Online: Jul. 22, 2022

    The Author Email: Xingsheng Deng (whudxs@163.com)

    DOI:10.3788/LOP202259.1628004

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