外观
论文arXiv 2609.291861 个月前
面向下游岩土工程分析的多时相采场点云自动地理配准技术
原文链接：An Automated Georeferencing Technique for Multi-Temporal Stope Point Clouds for Downstream Geotechnical Analysis（arXiv 2609.29186 · 发布日期：2026-09-24）
An Automated Georeferencing Technique for Multi-Temporal Stope Point Clouds for Downstream Geotechnical Analysis
针对地下矿山多时相采场点云需手工地理配准的问题，提出3D-TARGeT并在四次采场扫描上验证。
核心要点
- GNSS受限的地下环境中，独立采集的采场点云处于局部坐标系，手工对齐耗时。
- 3D-TARGeT利用低成本通用非唯一矩形标签，结合自动识别、几何匹配和刚体变换估计。
- 概念验证使用一个地下矿山采场的四个多时相点云扫描，标签在代表性扫描条件下模拟。
- 各扫描中位云到云距离和RMSE均低于0.03米，并优于常用自动点云配准技术。
验证范围与局限：仅在一个地下矿山采场的四个多时相点云扫描上做概念验证，标签为模拟。
英文摘要 (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.
作者：Dibyayan Patra, Simit Raval, Pasindu Ranasinghe, Bikram Banerjee, Ismet Canbulat
来源
本页事实对应的公开页面，以原文为准。