An Automated Georeferencing Technique for Multi-Temporal Stope Point Clouds for Downstream Geotechnical Analysis

2026-09-24

AI Summary

This study addresses the inefficiency of manual registration for multi-temporal stope point clouds in underground GNSS-denied environments, which significantly hinders data utilization. To overcome this limitation, we propose 3D-TARGeT, an automated point cloud registration framework. This method innovatively employs low-cost, non-unique rectangular tags instead of manual alignment, achieving automatic registration and geo-referencing through tag detection, geometric matching, and rigid transformation estimation. Experimental results demonstrate that the proposed framework attains centimeter-level registration accuracy (RMSE < 0.03 m), substantially outperforming conventional automated registration approaches. By significantly enhancing automation robustness while effectively reducing reliance on manual intervention, 3D-TARGeT provides a practical and efficient solution for multi-temporal point cloud analysis in challenging underground mining environments.

Abstract

The increasing use of UAV laser scanning in underground mines has enabled frequent acquisition of 3D point clouds from challenging environments such as stopes, generating large volumes of multi-temporal spatial data throughout successive excavation stages. However, in GNSS-denied underground environments, independently acquired stope point clouds are generated within local scanner reference frames and require registration and georeferencing before integration with mine reference data for downstream geotechnical analysis, monitoring, and mine planning. This process is commonly performed manually by aligning individual stope scans with mine reference drives, making repeated georeferencing time-consuming and potentially limiting the utilisation of routinely acquired data. This study proposes the 3D Tag-based Automated Registration and Georeferencing Technique (3D-TARGeT), an automated framework using low-cost, generic, non-unique rectangular tags to establish spatial correspondence between stope point clouds and the mine reference coordinate system. The framework combines automated tag identification, geometric tag matching, and rigid transformation estimation. It was evaluated as a proof of concept using four multi-temporal point-cloud scans of an underground mine stope, with the proposed tags simulated under representative scanning conditions. 3D-TARGeT achieved consistent centimetre-level georeferencing accuracy, with median cloud-to-cloud distance and root mean square error below 0.03 m across all scans, while substantially outperforming widely used automatic point-cloud registration techniques. Overall, 3D-TARGeT provides an accurate and robust approach for automating stope point-cloud georeferencing, reducing reliance on manual alignment and facilitating multi-temporal datasets for downstream geological and geotechnical applications.

Problem

• georeferencing
• multi-temporal point clouds
• underground mine stope
• GNSS-denied environment
• point cloud registration

Innovation

• Automated Georeferencing
• Point Cloud Registration
• 3D-TARGeT
• Rectangular Tags
• Multi-temporal Point Clouds