Toward a Mine-Wide, Real-Time, and Autonomous Geotechnical Change Detection, Monitoring, and Prediction Framework for Underground Mines

Adverse ground behavior events, such as convergence and falls of ground, are some of the most significant challenges and hazards in underground mines. Our goal is to work toward mine-wide, digital, and real-time three-dimensional geotechnical situational awareness and predictive capabilities in complex underground mining operations. This will benefit operators, engineers, and management by enabling them to develop a more proactive approach to ground control. To this end, and inspired by research in the field of remote sensing and autonomous robotics, this paper assesses the potential of mobile light detection and ranging (LiDAR) data acquisition for high-frequency, high-accuracy monitoring tasks in Global Navigation Satellite Systems (GNSS) deprived underground mines. Limitations of current monitoring practices are reviewed. Relevant academic and commercial developments in the fields of indoor mobile mapping systems (MMSs) and simultaneous localization and mapping (SLAM), robust autonomous robotic platforms, and automated remote sensing data processing are highlighted, and their current shortfalls are evaluated. To conclude, the ramifications of this work for the development of future autonomous underground mine monitoring solutions are discussed.