Digital modelling method of coal-mine roadway based on millimeter-wave radar array

Area of Science:

• Geoscience and Mining Engineering
• Sensor Technology and Data Processing
• Computational Geometry and 3D Modeling

Background:

• Coal mine roadways present challenges for data acquisition due to narrow spaces, low/uneven illumination, dust, and water mist.
• Traditional vision and laser sensors exhibit poor accuracy and reliability in these harsh underground environments.
• Existing methods struggle with complex environmental interference and incomplete scanning by single sensors.

Purpose of the Study:

• To propose a novel digital modeling method for coal mine roadways using a millimeter-wave radar array.
• To address limitations of existing sensors and environmental interferences for accurate 3D reconstruction.
• To provide a new research methodology for underground roadway digital modeling.

Main Methods:

• Developed a multi-layer filtering noise reduction and dynamic subgraph registration for millimeter-wave point clouds to handle environmental interference.
• Constructed a millimeter-wave radar array acquisition system and an improved Iterative Closest Point (ICP) algorithm for point cloud fusion.
• Employed Poisson surface reconstruction with density-weighted interpolation to refine the roadway structure and create a digital model.

Main Results:

• The millimeter-wave radar array method accurately captures roadway surrounding rock information.
• Achieved a 22.4% increase in roadway point cloud density.
• Demonstrated low average absolute error percentages for reconstructed roadway width (0.82%) and height (0.72%).

Conclusions:

• The proposed millimeter-wave radar array digital modeling method is effective for accurate coal mine roadway reconstruction.
• The method significantly improves data density and model accuracy compared to existing approaches.
• Provides a robust solution for digital modeling in challenging underground mining environments.