一种用于下游岩土工程分析的多时相采场点云自动地理配准技术

中文摘要 AI 辅助

地下矿山中无人机激光扫描的日益普及，使得能够从采场等具有挑战性的环境中频繁获取三维点云，并在 successive excavation stages（连续开挖阶段）中生成大量多时相空间数据。然而，在无GNSS（全球导航卫星系统）的地下环境中，独立获取的采场点云是在局部扫描仪参考坐标系中生成的，需要经过配准和地理配准后才能与矿山参考数据集成，用于下游岩土工程分析、监测和矿山规划。这一过程通常通过手动将单个采场扫描与矿山参考巷道对齐来完成，使得重复的地理配准耗时费力，并可能限制常规获取数据的利用。本研究提出了基于三维标签的自动配准与地理配准技术（3D-TARGeT），这是一种利用低成本、通用、非唯一矩形标签建立采场点云与矿山参考坐标系之间空间对应关系的自动化框架。该框架结合了自动标签识别、几何标签匹配和刚体变换估计。作为概念验证，该框架使用地下矿山采场的四次多时相点云扫描进行了评估，其中所提出的标签在代表性扫描条件下进行了模拟。3D-TARGeT实现了稳定的厘米级地理配准精度，所有扫描的中位点云间距离和均方根误差均低于0.03米，同时显著优于广泛使用的自动点云配准技术。总体而言，3D-TARGeT为自动化采场点云地理配准提供了一种准确且稳健的方法，减少了对手动对齐的依赖，并促进了多时相数据集在下游地质和岩土工程应用中的利用。

英文摘要

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.

发表机构

• University of New South Wales（新南威尔士大学）
• University of Southern Queensland（南昆士兰大学）

机构由 AI 辅助整理，请以论文原文为准。