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Robust GICP-Based 3D LiDAR SLAM for Underground Mining Environment.

Zhuli Ren1,2, Liguan Wang3,4, Lin Bi5,6

  • 1School of Resources and Safety Engineering, Central South University, Changsha 410083, China. renzhuli@yeah.net.

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Summary

This study introduces a new graph simultaneous localization and mapping (SLAM) method using Generalized Iterative Closest Point (GICP) for unmanned mining. It achieves accurate underground localization and mapping, improving mine safety and efficiency.

Keywords:
GICPSLAMgraph optimizationloop detectionroadway planeunderground mine

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Area of Science:

  • Robotics and Automation
  • Geospatial Information Systems
  • Mining Engineering

Background:

  • Unmanned mining enhances safety and efficiency but requires precise localization and mapping in complex underground environments.
  • Existing Simultaneous Localization and Mapping (SLAM) methods face challenges in underground settings due to sensor limitations and environmental variability.

Purpose of the Study:

  • To develop a novel graph SLAM optimization method for accurate localization and mapping in underground mining environments.
  • To improve the robustness and efficiency of SLAM systems for unmanned mining applications.

Main Methods:

  • A Generalized Iterative Closest Point (GICP) based 3D point cloud registration is employed for frame-to-frame, key frame-to-frame, and loop closure.
  • Roadway plane constraints and loop constraints are integrated into the pose graph optimization.
  • A lightweight loop detection and optimization strategy is implemented, combined with noise point cloud removal.

Main Results:

  • The proposed GICP-based SLAM method demonstrates low drift localization and effective point cloud map construction.
  • Evaluation in diverse underground scenes, including a mine laboratory, validates the algorithm's performance.
  • Comparison with existing methods like Lidar Odometry and Mapping (LOAM) shows competitive or superior results.

Conclusions:

  • The novel graph SLAM approach provides a robust solution for accurate localization and navigation in underground mining.
  • This method offers significant technical support for the advancement of autonomous systems in the mining industry.