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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