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Feasibility Domain Construction and Characterization Method for Intelligent Underground Mining Equipment Integrating

Siya Sun1,2, Xiaotong Han1, Hongwei Ma2,3

  • 1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
Summary

This study introduces a new method for creating 3D digital maps of underground coal mines using RGB-D cameras and ORB-SLAM3. The approach enhances environmental perception and creates efficient, accurate 3D feasible space representations for autonomous operations.

Keywords:
feasible domain constructionsimultaneous localization and mappingunderground coal mine

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Area of Science:

  • Robotics and Computer Vision
  • Geospatial Information Science
  • Mining Engineering

Background:

  • Underground coal mines present significant challenges for environmental perception due to dust, poor lighting, and confined spaces.
  • Existing methods struggle with consistent and efficient representation of feasible workspace domains in these harsh conditions.
  • Accurate 3D spatial understanding is crucial for autonomous navigation, path planning, and safety in mining operations.

Purpose of the Study:

  • To develop a robust digital spatial construction and representation method for underground mining environments.
  • To overcome limitations in environmental perception and feasible domain representation caused by challenging underground conditions.
  • To provide a reliable 3D digital spatial foundation for autonomous mining applications.

Main Methods:

  • Integration of RGB-D depth vision with ORB-SLAM3 for enhanced spatial mapping.
  • Development of a "dense-sparse cooperative" OAK-DenseMapper Pro module for high-quality point-cloud reconstruction.
  • Conversion of dense point clouds to a memory-efficient probabilistic octree occupancy map for scalable 3D representation.

Main Results:

  • Achieved high-precision RGB-D camera calibration using ChArUco boards, improving geometric parameter reliability.
  • Demonstrated significant improvements in dense mapping point count (approx. 38% increase) and reduced trajectory errors (up to 10% reduction).
  • Reduced map memory footprint to approximately 0.5% of the original point cloud with efficient octree conversion.

Conclusions:

  • The proposed method enables real-time, efficient, and consistent 3D feasible domain representation in complex underground coal mines.
  • The digital spatial foundation supports critical applications such as path planning and safe obstacle avoidance.
  • This approach enhances the reliability and efficiency of autonomous operations in challenging subterranean environments.