Analysis of SLAM-Based Lidar Data Quality Metrics for Geotechnical Underground Monitoring

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