Adverse ground behavior events, such as convergence and ground falls, pose critical risks to underground mine safety and productivity. Today, monitoring of such failures is primarily conducted using legacy techniques with low spatial and temporal resolution while exposing workers to hazardous environments. This study assesses the potential of novel simultaneous localization and mapping (SLAM)-based light detection and ranging (Lidar) data quality for rapid, digital, and eventually autonomous mine-wide underground geotechnical monitoring. We derive a comprehensive suite of quality metrics based on tests in two underground mines for two state-of-the-art mobile laser scanning (MLS) systems. Our results provide evidence that SLAM-based MLS provides data of the quality required to detect geotechnically relevant changes while being significantly more efficient for large mine layouts when compared to traditional static systems. Additionally, we show that SLAM-specific processing can achieve an order of magnitude better relative accuracy relevant for change detection than quality metrics derived from traditionally deployed tests would suggest while reducing SLAM drift error by up to 90%. In collaboration with an operating block cave mine, we confirm these capabilities in field tests on a mine-wide scale and, for the first time, demonstrate methods of rockfall detection using MLS data. While more work is required to investigate optimal collection, processing, and utilization of MLS data, we demonstrate its potential to become an effective and widely applicable data source for rapid, accurate, and comprehensive geotechnical inspections.

Convergence and rockmass failure are significant hazards to personnel and physical assets in underground tunnels, caverns, and mines. Mobile Laser Scanning Systems (MLS) can deliver large volumes of point cloud data at a high frequency and on a large scale. However, current change detection approaches do not deliver sufficient sensitivity and precision for real-time performance on large-scale datasets. We present a novel, octree-based computational framework for intra-voxel statistical inference change detection and deformation analysis. Our approach exploits high-density MLS data to test for statistical significance for appearing objects caused by rockfall and for low-magnitude deformations, such as convergence. In field tests, our method detects rock falls with side lengths as small as 0.03 m and convergence as low as 0.01 m, or 0.5% wall-to-wall strain. When compared against a state-of-the-art multi-scale model-to-model cloud comparison (M3C2)-based method, ours is less sensitive to noisy data and parameter selection while also requiring fewer parameters. Most notably, our method is the only one tested that can perform real-time change detection on large-scale datasets on a single processor thread. Our method achieves a computational improvement of 50 times over single-threaded M3C2 while maintaining a performance scalability that is four times greater with dataset size. Our framework shows significant potential to enable accurate real-time geotechnical monitoring of large-scale underground spaces.