January 2023
Volume 75    Issue 1

Analysis of SLAM-based lidar data quality metrics for geotechnical underground monitoring

Fahle, Lukas; Holley, Elizabeth A.; Walton, Gabriel; Petruska, Andrew J.; Brune, Jurgen F.


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 minewide 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 systems provide 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 percent. In collaboration with an operating block-caving mine, we confirm these capabilities in field tests on a minewide 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.

Full-text paper:
Mining, Metallurgy & Exploration (2022) 39:1939–1960, https://doi.org/10.1007/s42461-022-00664-3


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