Abstract
Spatially referenced and geometrically accurate laser scans are essential for mapping and monitoring applications in underground mines to ensure safe and smooth operation. However, obtaining an absolute 3D map in an underground mine environment is challenging using laser scanning due to the unavailability of global navigation satellite system (GNSS) signals. Consequently, applications that require georeferenced point cloud or coregistered multitemporal point clouds such as detecting changes, monitoring deformations, tracking mine logistics, measuring roadway convergence rate and evaluating construction performance become challenging. Current mapping practices largely include a manual selection of discernable reference points in laser scans for georeferencing and coregistration which is often time-consuming, arduous and error-prone. Moreover, challenges in obtaining a sensor positioning framework, the presence of structurally symmetric layouts and highly repetitive features (such as roof bolts) makes the multitemporal scans difficult to georeference and coregister. This study aims at overcoming these practical challenges through development of three-dimensional unique identifiers (3DUIDs) and a 3D registration (3DReG) workflow. Field testing of the developed approach in an underground coal mine has been found effective with an accuracy of 1.76 m in georeferencing and 0.16 m in coregistration for a scan length of 850 m. Additionally, automatic extraction of mine roadway profile has been demonstrated using 3DUID which is often a compliant and operational requirement for mitigating roadway related hazards that includes roadway convergence rate, roof/rock falls, floor heaves and vehicle clearance for collision avoidance. Potential applications of 3DUID include roadway profile extraction, guided automation, sensor calibration, reference targets for a routine survey and deformation monitoring.
Highlights
Point clouds obtained from terrestrial or mobile laser scanning play a critical role in infrastructure mapping and development projects, and are well suited for civil, mining and transportation industries
This study develops and proposes a three dimensional unique identifier, referred to as 3DUID, to solve georeferencing and data coregistration of point clouds in sensitive underground mines for automated monitoring applications
The study consisted of three main steps: (1) development of 3DUID based on mapping characteristics of employed laser scanner, (2) development of 3D registration (3DReG) workflow for georeferencing and coregistration of multitemporal point clouds, and (3) field demonstration of automated georeferencing, coregistration and roadway profile extraction
Summary
Point clouds obtained from terrestrial or mobile laser scanning play a critical role in infrastructure mapping and development projects, and are well suited for civil, mining and transportation industries. A few distinguishable control points are installed inside the GNSS-denied environment and are tagged by a total station survey to obtain global three-dimensional (3D) coordinates. Other studies have partially automated the process by georeferencing point clouds using coregistration of subsequent laser frames by matching installed control points [8,9]. The accuracy of georeferencing usually depends on how correctly the control points are identified in the LiDAR data and has an irrefutable impact on localisation and change detection capability in multitemporal data [10,11]. Environments with symmetrical and repeatable layouts, such as an underground mine, lead to aliasing issues, i.e., false laser scan matches in coregistration due to the absence of an adequate number of distinct features [13]. Point descriptors though mostly effective are computationally intensive to generate and can produce unreliable results for environments with a low number of distinguishable features
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