• Laser & Optoelectronics Progress
  • Vol. 59, Issue 4, 0428002 (2022)
Lijun Ren1, Yuansheng Liu2、*, and Kedi Zhong1
Author Affiliations
  • 1Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
  • 2Beijing Engineering Research Center of Smart Mechanical Innovation Design Service, Beijing 100101, China
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    DOI: 10.3788/LOP202259.0428002 Cite this Article Set citation alerts
    Lijun Ren, Yuansheng Liu, Kedi Zhong. Building Method of Semantic Map Based on Improved PFPN[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0428002 Copy Citation Text show less
    Block diagram of semantic map construction
    Fig. 1. Block diagram of semantic map construction
    Framework of PFPN semantic segmentation network
    Fig. 2. Framework of PFPN semantic segmentation network
    Structure of reduced PFPN
    Fig. 3. Structure of reduced PFPN
    Schematic diagram of coordinate system
    Fig. 4. Schematic diagram of coordinate system
    Time matching method of Lidar and camera
    Fig. 5. Time matching method of Lidar and camera
    Fusion result
    Fig. 6. Fusion result
    Construction of semantic map
    Fig. 7. Construction of semantic map
    Experimental test route and test environment. (a) Test route; (b) test environment
    Fig. 8. Experimental test route and test environment. (a) Test route; (b) test environment
    Map comparison. (a) SLAM map established by original point cloud; (b) semantic map after removing vehicle information in environment; (c) (d) enlarged display of rectangular area in Fig. 9 (a),(b)
    Fig. 9. Map comparison. (a) SLAM map established by original point cloud; (b) semantic map after removing vehicle information in environment; (c) (d) enlarged display of rectangular area in Fig. 9 (a),(b)
    Mapping track
    Fig. 10. Mapping track
    Error comparison between x-axis and y-axis
    Fig. 11. Error comparison between x-axis and y-axis
    Comparison of overall pose error
    Fig. 12. Comparison of overall pose error
    MethodBackbonemIoU /%Time /s
    DANetResNet-10139.7
    HRNetHRNet42.73.89
    PSPNetResNet-5039.90.36
    PFPNResNet-101-3×42.10.28
    PFPNResNet-50-3×40.20.21
    PFPNResNet-50-1×41.20.18
    Proposed methodResNet-50-1×41.00.13
    Table 1. Comparison of different methods
    MethodNnumber of point clouds
    Original method2089
    Proposed method1258
    Change831
    Table 2. Average number of point clouds per frame
    MethodTime /ms
    Original method53
    Proposed method31
    Change22
    Table 3. Comparison of average single registration time
    ParameterOriginal methodProposed methodChange
    x /m0.43880.32980.1090
    y /m0.35080.23710.1137
    P /rad0.24580.16140.0844
    Table 4. Average error analysis of key parameters
    Lijun Ren, Yuansheng Liu, Kedi Zhong. Building Method of Semantic Map Based on Improved PFPN[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0428002
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