Volume 50 Issue 3
Mar.  2024
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(3): 48-55
LIAN Zhongwen, REN Zhuli, HAO Yinghao, et al. A point cloud denoising method for unstructured roadways based on regional growth[J]. Journal of Mine Automation，2024，50（3）：48-55.  doi: 10.13272/j.issn.1671-251x.2024010037
Citation: LIAN Zhongwen, REN Zhuli, HAO Yinghao, et al. A point cloud denoising method for unstructured roadways based on regional growth[J]. Journal of Mine Automation，2024，50（3）：48-55.  doi: 10.13272/j.issn.1671-251x.2024010037
shu

A point cloud denoising method for unstructured roadways based on regional growth

doi: 10.13272/j.issn.1671-251x.2024010037
  • 1.

    School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China

  • 2.

    Hulusu Coal Mine, Coal Branch of ZTHC Energy Limited Liability Company, Ordos 017000, China

  • 3.

    Henan Key Laboratory for Green and Efficient Mining & Comprehensive Utilization of Mineral Resources, Henan Polytechnic University, Jiaozuo 454003, China

  • 4.

    Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization, Jiaozuo 454003, China

  • Received Date: 2024-01-12
  • Rev Recd Date: 2024-03-15
    • Available Online: 2024-04-11
    Abstract
  • Currently, research on point cloud denoising in underground roadways has not fully met the special denoising needs of roadway point clouds. Especially in narrow, enclosed, and complex underground roadway environments, the research has not fully addressed the challenges caused by pipe wall attachments, dust, and human noise. By analyzing the unstructured scenes and sensor errors underground, considering the noise caused by personnel, mobile devices, and pipeline networks, a point cloud denoising method for unstructured roadways based on region growth is proposed. The method uses 3D laser scanning technology to obtain 3D point cloud information of underground roadway scenes, and analyzes the abnormal points caused by unstructured underground scenes and sensor errors, as well as the noise features formed by personnel, mobile devices, and air and water pipelines. The method uses k-dimensional trees (kd-tree) to construct the topological relationship of point clouds, selects appropriate seed nodes and growth criteria, and sets appropriate curvature and angle thresholds. The method implements effective segmentation of roadway point clouds through region growth algorithms, and removes outlier point clouds that have not been added to the segmentation area. Based on the features of noise, further denoising optimization is carried out based on the segmentation results of the roadway point cloud region. The experimental results indicate that for situations where there are features such as personnel and equipment in the roadway, it is recommended to set the angle threshold of the region growth algorithm to around 10° and the curvature threshold to around 3. In practical applications, it is necessary to balance the reduction of data volume with the denoising effect to ensure the effectiveness of data processing and improve data quality. When using a region growth based unstructured roadway point cloud denoising method for denoising, the reduction in point cloud quantity is between SOR filter and low-pass filter, which can effectively remove noise such as personnel and equipment.

     

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    • References(27)
  • [1]
    王国法,杜毅博. 智慧煤矿与智能化开采技术的发展方向[J]. 煤炭科学技术,2019,47(1):1-10.

    WANG Guofa,DU Yibo. Development direction of intelligent coal mine and intelligent mining technology[J]. Coal Science and Technology,2019,47(1):1-10.
    [2]
    王国法,刘峰,孟祥军,等. 煤矿智能化(初级阶段)研究与实践[J]. 煤炭科学技术,2019,47(8):1-36.

    WANG Guofa,LIU Feng,MENG Xiangjun,et al. Research and practice on intelligent coal mine construction(primary stage)[J]. Coal Science and Technology,2019,47(8):1-36.
    [3]
    余润泽,单伽锃,李培振,等. 基于三维点云的工程结构平面分割及表面检测[J]. 中南大学学报(自然科学版),2023,54(6):2233-2243. doi: 10.11817/j.issn.1672-7207.2023.06.014

    YU Runze,SHAN Jiazeng,LI Peizhen,et al. Point cloud-based plane segmentation and surface inspection of engineering structures[J]. Journal of Central South University(Science and Technology),2023,54(6):2233-2243. doi: 10.11817/j.issn.1672-7207.2023.06.014
    [4]
    KUMAR S S,PRATAP B B,SIMIT R. A review of laser scanning for geological and geotechnical applications in underground mining[J]. International Journal of Mining Science and Technology,2023,33(2):133-154. doi: 10.1016/j.ijmst.2022.09.022
    [5]
    戴文祥,陈雷,闫鹏飞,等. 基于三维激光扫描的煤矿巷道形变监测方法[J]. 工矿自动化,2023,49(10):61-67,95.

    DAI Wenxiang,CHEN Lei,YAN Pengfei,et al. A deformation monitoring method for coal mine roadway based on 3D laser scanning[J]. Journal of Mine Automation,2023,49(10):61-67,95.
    [6]
    RUSU R B,COUSINS S. 3D is here:point cloud library (PCL)[C]. IEEE International Conference on Robotics and Automation,Shanghai,2011:6324-6327.
    [7]
    KOLLURI R. Provably good moving least squares[C]. The 16th Annual ACM-SIAM Symposium on Discrete Algorithms,Vancouver,2008:1008-1017.
    [8]
    LIPMAN Y,COHEN-OR D,LEVIN D,et al. Parameterization-free projection for geometry reconstruction[J]. ACM Transactions on Graphics(TOG),2007,26(3). DOI: 10.1145/1276377.1276405.
    [9]
    HUANG Hui,WU Shihao,GONG Minglun,et al. Edge-aware point set resampling[J]. ACM Transactions on Graphics,2013,32(1):1-12.
    [10]
    HUANG Hui,LI Dan,ZHANG Hao,et al. Consolidation of unorganized point clouds for surface reconstruction[J]. ACM Transactions on Graphics,2009,28(5):1-7.
    [11]
    MATTEI E,CASTRODAD A. Point cloud denoising via moving RPCA[J]. Computer Graphics Forum:Journal of the European Association for Computer Graphics,2017,36(8):123-137.
    [12]
    SUN Yujing,SCHAEFER S,WANG Wenping. Denoising point sets via L0 minimization[J]. Computer Aided Geometric Design,2015,35:2-15.
    [13]
    ROVERI R,ÖZTIRELI A C,PANDELE I,et al. Pointpronets:consolidation of point clouds with convolutional neural networks[J]. Computer Graphics Forum,2018,37(2):87-99. doi: 10.1111/cgf.13344
    [14]
    ZHANG Dongbo,LU Xuequan,QIN Hong,et al. Pointfilter:point cloud filtering via encoder-decoder modeling[J]. IEEE Transactions on Visualization and Computer Graphics,2020,27(3):2015-2027.
    [15]
    RAKOTOSAONA M J,LA BARBERA V,GUERRERO P,et al. Pointcleannet:learning to denoise and remove outliers from dense point clouds[J]. Computer Graphics Forum,2020,39(1):185-203. doi: 10.1111/cgf.13753
    [16]
    CASAJUS P H,RITSCHEL T,ROPINSKI T. Total denoising:unsupervised learning of 3D point cloud cleaning[C]. IEEE/CVF International Conference on Computer Vision,Seoul,2019:52-60.
    [17]
    JONES T R. Feature preserving smoothing of 3D surface scans[D]. Cambridge:Massachusetts Institute of Technology,2003.
    [18]
    FLEISHMAN S,DRORI I,COHEN-OR D. Bilateral mesh denoising[J]. ACM Transactions on Graphics,2003,22(3):950-953. doi: 10.1145/882262.882368
    [19]
    VOLLMER J,MENCL R,MUELLER H. Improved laplacian smoothing of noisy surface meshes[J]. Computer Graphics Forum,1999,18(3):131-138. doi: 10.1111/1467-8659.00334
    [20]
    DESBRUN M,MEYER M,SCHRODER P,et al. Implicit fairing of irregular meshes using diffusion and curvature flow[C]. The 26th Annual Conference on Computer Graphics and Interactive Techniques,1999:317-324. DOI: 10.1145/311535.311576.
    [21]
    COMANICIU D,RAMESH V,MEER P. The variable bandwidth mean shift and data-driven scale selection[C]. The 8th IEEE International Conference on Computer Vision,Vancouver,2001:438-445.
    [22]
    GEORGESCU B,SHIMSHONI I,MEER P. Mean shift based clustering in high dimensions:a texture classification example[C]. The 9th IEEE International Conference on Computer Vision,Nice,2003:456-463.
    [23]
    COMANICIU D,MEER P. Mean shift analysis and applications[C]. The 7th IEEE International Conference on Computer Vision,Kerkyra,1999:1197-1203.
    [24]
    XU Xiangyang,YANG Hao,NEUMANN I. Time-efficient filtering method for three-dimensional point clouds data of tunnel structures[J]. Advances in Mechanical Engineering,2018,10(5). DOI: 10.1177/16878140187731.
    [25]
    余鹏磊,于海洋,谢秋平,等. 基于RANSAC算法的地铁隧道点云数据滤波[J]. 测绘工程,2014,23(8):28-31. doi: 10.3969/j.issn.1006-7949.2014.08.007

    YU Penglei,YU Haiyang,XIE Qiuping,et al. Filtering for subway tunnel point cloud data based on RANSAC algorithm[J]. Engineering of Surveying and Mapping,2014,23(8):28-31. doi: 10.3969/j.issn.1006-7949.2014.08.007
    [26]
    ZHU Ningning,JIAA Y,LUO Lun. Tunnel point cloud filtering method based on elliptic cylindrical model[J]. The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2016,41:735-740.
    [27]
    陈建华,马宝,王蒙. 基于二次特征提取的煤矿巷道表面点云数据精简方法[J]. 工矿自动化,2023,49(12):114-120.

    CHEN Jianhua,MA Bao,WANG Meng. A method for simplifying surface point cloud data of coal mine roadways based on secondary feature extraction[J]. Journal of Mine Automation,2023,49(12):114-120.
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