Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

Abstract

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.

Application of artificial intelligence in mine ventilation: a brief review

“…Machine vision was utilized to process measurement data of the geometry of mine airways and mining equipment using various sensors: visual, inertial, LiDAR, and their combinations (Artan et al, 2011 ; Jiang et al, 2019 ; Zhai et al, 2020 ; Singh et al, 2023 ). This information can be further used to estimate changes in mine air resistance (Wong et al, 2011 ; Lavigne and Marshall, 2012 ; Watson and Marshall, 2018 ; Fahle et al, 2023 ).…”

An Accurate and Efficient Supervoxel Re-Segmentation Approach for Large-Scale Point Clouds Using Plane Constraints

“…The redundancy, rotational invariance, and lack of topological relationships between points in point clouds make it a powerful and flexible medium for representing the surface morphology of objects. These advantages have led to progress in various areas, such as building feature extraction [3], complex terrain analyses [4,5], forest investigations [6], deformation measurement [7], and structural health monitoring and assessment [8] using point clouds.…”