• Laser & Optoelectronics Progress
  • Vol. 59, Issue 16, 1628004 (2022)
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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    DOI: 10.3788/LOP202259.1628004 Cite this Article Set citation alerts
    Guo Tang, Xingsheng Deng, Qingyang Wang. Point Cloud Filtering Algorithm Based on Density Clustering[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1628004 Copy Citation Text show less
    Schematic diagram of DBSCAN
    Fig. 1. Schematic diagram of DBSCAN
    Flow chart of point cloud filtering algorithm based on density clustering
    Fig. 2. Flow chart of point cloud filtering algorithm based on density clustering
    Simulation map of laser point cloud position in large survey area
    Fig. 3. Simulation map of laser point cloud position in large survey area
    3D view of S53
    Fig. 4. 3D view of S53
    Elevation map of S53
    Fig. 5. Elevation map of S53
    Filtering result map of S53
    Fig. 6. Filtering result map of S53
    Error point location map of S53
    Fig. 7. Error point location map of S53
    Relief image after filtering of S53
    Fig. 8. Relief image after filtering of S53
    Section plane comparison diagram of S53
    Fig. 9. Section plane comparison diagram of S53
    Schematic diagram of classification error
    Fig. 10. Schematic diagram of classification error
    3D view of S61
    Fig. 11. 3D view of S61
    Elevation map of S61
    Fig. 12. Elevation map of S61
    Filtering result map of S61
    Fig. 13. Filtering result map of S61
    Error point location map of S61
    Fig. 14. Error point location map of S61
    Relief image after filtering of S61
    Fig. 15. Relief image after filtering of S61
    Section plane comparison diagram of S61
    Fig. 16. Section plane comparison diagram of S61
    3D view of S21
    Fig. 17. 3D view of S21
    Filtering result map of S21
    Fig. 18. Filtering result map of S21
    Relief image after filtering of S21
    Fig. 19. Relief image after filtering of S21
    Error point location map of S21
    Fig. 20. Error point location map of S21
    Mutation point location map of S21
    Fig. 21. Mutation point location map of S21
    Re-division result map of S21
    Fig. 22. Re-division result map of S21
    Relief image before filtering of S24
    Fig. 23. Relief image before filtering of S24
    Elevation map of S24
    Fig. 24. Elevation map of S24
    Filtering result map of S24
    Fig. 25. Filtering result map of S24
    Error point location map of S24
    Fig. 26. Error point location map of S24
    Relief image after filtering of S24
    Fig. 27. Relief image after filtering of S24
    Section plane comparison diagram of S24
    Fig. 28. Section plane comparison diagram of S24
    3D view of S71
    Fig. 29. 3D view of S71
    Filtering result map of S71
    Fig. 30. Filtering result map of S71
    Relief image after filtering of S71
    Fig. 31. Relief image after filtering of S71
    Error point location map of S71
    Fig. 32. Error point location map of S71
    Mutation point location map of S71
    Fig. 33. Mutation point location map of S71
    Re-division result map of S71
    Fig. 34. Re-division result map of S71
    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 1. Definition of filtering error
    SampleTⅠTⅡTE
    S5315.4024.9115.78
    Table 2. Filter error statistics of S53
    SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
    S5348.4520.198.9112.6052.8117.2927.2437.0728.0715.78
    Table 3. Comparison of S53 total filtering error with other filtering algorithms
    SampleTⅠTⅡTE
    S615.5615.845.91
    Table 4. Filter error statistics of S61
    SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
    S6135.872.992.086.9121.6818.9913.4721.6315.455.91
    Table 5. Comparison of S61 total filtering error with other filtering algorithms
    SampleTⅠTⅡTE
    S210.4311.722.93
    Table 6. Filter error statistics of S21
    SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
    S218.538.804.252.579.309.844.557.766.952.93
    Table 7. Comparison of S21 total filtering error with other filtering algorithms
    SampleTⅠTⅡTE
    S2410.4914.9211.71
    Table 8. Filter error statistics of S24
    SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
    S2413.8313.334.428.6436.0623.2511.5325.2817.0411.71
    Table 9. Comparison of S24 total filtering error with other filtering algorithms
    SampleTⅠTⅡTE
    S7115.2610.1714.68
    Table 10. Filter error statistics of S71
    SampleElmqvistSohnAxelssonPfeiferBrovelliRoggeroWackSitholeMeanProposed algorithm
    S7134.222.201.638.8534.985.1116.9721.8315.7214.68
    Table 11. Comparison of S53 total filtering error with other filtering algorithms
    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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