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A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data
Jitong Kang1, Mei Li1, Shanjun Mao1
1Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China.
Sensors (Basel, Switzerland)
|April 13, 2024
Summary
This study introduces a handheld 3D laser scanner method for accurate underground coal mine tunnel deformation detection. The approach enhances safety and efficiency in monitoring tunnel integrity using mobile laser scanning technology.
Area of Science:
- Geotechnical Engineering
- Mining Engineering
- Geospatial Technology
Background:
- Tunnel deformation detection is critical for coal mine safety management.
- Traditional methods are labor-intensive and pose risks in confined spaces.
- Stationary 3D laser scanning is time-consuming and operationally hazardous.
Purpose of the Study:
- To propose an efficient and accurate method for detecting underground coal mine tunnel deformation.
- To leverage handheld 3D laser scanning and SLAM technology for comprehensive point cloud acquisition.
- To analyze tunnel deformation from both global and cross-sectional perspectives.
Main Methods:
- Utilized SLAM (Simultaneous Localization and Mapping) laser radar for complete tunnel point cloud data acquisition.
- Projected 3D point clouds onto planes to determine tunnel centerline coordinates.
- Matched multi-temporal 3D point cloud data to a unified coordinate system using the derived centerline for deformation analysis.
Main Results:
- The proposed method successfully obtained complete point cloud information of underground tunnels.
- Tunnel deformation parameters were accurately analyzed from global and cross-sectional viewpoints.
- On-site data collection validated the algorithm's feasibility and superior accuracy compared to existing methods.
Conclusions:
- The handheld 3D laser scanning method provides a more accurate and practical solution for coal mine tunnel deformation detection.
- This mobile laser scanning approach enhances operational safety and efficiency in high-risk underground environments.
- The algorithm meets practical requirements and offers improved performance over traditional and other advanced techniques.
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