Multi-Sensor Data Fusion for Real-Scene 3D Reconstruction and Digital Twin Visualization in Coal Mine Tunnels

Researchers at the China University of Mining and Technology-Beijing have proposed a multi-sensor data-fusion-based method for real-scene 3D reconstruction and digital twin visualization of coal mine tunnels. The approach aims to address issues such as low accuracy in non-photorealistic modeling and difficulties in feature object recognition during traditional coal mine digitization processes. This breakthrough could significantly enhance the accuracy and practicality of photorealistic 3D modeling in intelligent mining applications.

Key Takeaways:

• The research employs cubemap-based mapping technology to project acquired real-time tunnel images onto six faces of a cube, combined with navigation information, pose data, and synchronously acquired point cloud data to achieve spatial alignment and data fusion.
• Inner/outer corner detection algorithms are utilized for precise image segmentation, and a point cloud region growing algorithm integrated with information entropy optimization is proposed to realize complete recognition and segmentation of tunnel planes and high-curvature feature objects.
• Geometric dimensions extracted from segmentation results are used to construct 3D models, and real-scene images are mapped onto model surfaces via UV (U and V axes of texture coordinate) texture mapping technology, generating digital twin models with authentic texture details.
• The method performs excellently in both simulated and real coal mine environments, with models capable of faithfully reproducing tunnel spatial layouts and detailed features while supporting multi-view visualization.
• This approach provides efficient and precise technical support for digital twin construction, fine-grained structural modeling, and safety monitoring of coal mine tunnels.
• The research has been published in the journal Sensors, with the article available at https://doi-org.sdpl.idm.oclc.org/10.3390/s25196153.

Statistics:

• The research used cubemap-based mapping technology to project acquired real-time tunnel images onto six faces of a cube.
• The point cloud region growing algorithm integrated with information entropy optimization processed 100,000 points to achieve complete recognition and segmentation of tunnel planes and high-curvature feature objects.
• The method generated digital twin models with authentic texture details, capable of faithfully reproducing tunnel spatial layouts and detailed features while supporting multi-view visualization (e.g., bottom view, left/right rotated views, front view).
• The research aimed to address issues such as low accuracy in non-photorealistic modeling and difficulties in feature object recognition during traditional coal mine digitization processes.

Sources:

• Research on Multi-Sensor Data Fusion Based Real-Scene 3D Reconstruction and Digital Twin Visualization Methodology for Coal Mine Tunnels. Sensors, 2025,25(19):6153. (Sensors - http://www.mdpi.com/journal/sensors)
• Journal of Engineering. October 27, 2025; p 3363.