• Laser & Optoelectronics Progress
  • Vol. 60, Issue 2, 0228005 (2023)
Jiyun Zhang1, Jianjun Wang1,*, Xuhui Li1, Jiongyu Wang1..., Xiaoxiao Cheng1 and Guangbin Wang2|Show fewer author(s)
Author Affiliations
  • 1School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, Shandong, China
  • 2Shandong Through Train Technology Co., Ltd., Zibo 255000, Shandong, China
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    DOI: 10.3788/LOP212708 Cite this Article Set citation alerts
    Jiyun Zhang, Jianjun Wang, Xuhui Li, Jiongyu Wang, Xiaoxiao Cheng, Guangbin Wang. Optimization of Feature-Extraction Method for Stockpiled Materials Based on LiDAR[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0228005 Copy Citation Text show less
    Technology roadmap
    Fig. 1. Technology roadmap
    26 adjacency diagram of voxel and section
    Fig. 2. 26 adjacency diagram of voxel and section
    Two adjacent points converge. (a) Convex relation; (b) concave relation
    Fig. 3. Two adjacent points converge. (a) Convex relation; (b) concave relation
    Laser scanning point cloud simulation and its preprocessing. (a) Model cloud; (b) preprocessing
    Fig. 4. Laser scanning point cloud simulation and its preprocessing. (a) Model cloud; (b) preprocessing
    Simulation results of region growing algorithm
    Fig. 5. Simulation results of region growing algorithm
    Simulation point cloud. (a) Supervoxel clustering; (b) concave and convex clustering
    Fig. 6. Simulation point cloud. (a) Supervoxel clustering; (b) concave and convex clustering
    Point cloud data collected by LiDAR
    Fig. 7. Point cloud data collected by LiDAR
    Point cloud of mound pretreatment process. (a) Original cloud; (b) through filter; (c) statistical filter; (d) moving least squares(MLS)smooth
    Fig. 8. Point cloud of mound pretreatment process. (a) Original cloud; (b) through filter; (c) statistical filter; (d) moving least squares(MLS)smooth
    Results of regional growthing experiment
    Fig. 9. Results of regional growthing experiment
    Results of point cloud clustering. (a) Supervoxel clustering;(b) concave and convex clustering
    Fig. 10. Results of point cloud clustering. (a) Supervoxel clustering;(b) concave and convex clustering
    Experimental point cloudFos /%Fus /%
    Simulated point cloud(SPC)(Fig. 52.741.87
    SPC(Fig. 60.200.14
    Actual measured point cloud(APC)(Fig. 103.112.47
    Table 1. Quantitative analysis of segmentation results in Fig. 5, Fig. 6, and Fig.10
    Jiyun Zhang, Jianjun Wang, Xuhui Li, Jiongyu Wang, Xiaoxiao Cheng, Guangbin Wang. Optimization of Feature-Extraction Method for Stockpiled Materials Based on LiDAR[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0228005
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