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基于无人机点云数据的露天矿台阶特征线提取

Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data.

作者信息

Mao Yachun, Wang Hui, Cao Wang, Fu Yuwen, Fu Yanhua, He Liming, Bao Nisha

机构信息

School of Resources & Civil Engineering, Northeastern University, Shenyang 110819, China.

School of Architecture, Northeastern University, Shenyang 110819, China.

出版信息

Sensors (Basel). 2022 Jul 30;22(15):5706. doi: 10.3390/s22155706.

DOI:10.3390/s22155706
PMID:35957263
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9371066/
Abstract

Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity.

摘要

台阶特征线是绘制露天矿现状图的重要几何元素之一，高效、准确地自动提取和更新台阶特征线对露天矿剥离计划和分析具有重要意义。本研究提出了一种基于无人机（UAV）点云数据的露天矿台阶特征线自动提取方法。该方法主要用于解决诸如精度低、局部特征线丢失以及台阶特征线提取方法不连续等关键问题。该方法首先基于MLS算法对露天矿点云进行规则栅格重采样，然后通过检测重采样点云中的高程梯度变化来提取台阶特征点集，进一步利用种子生长跟踪算法追踪台阶特征控制节点，最后通过将空间曲线拟合到台阶特征控制节点来生成平滑的台阶特征线。结果表明，该方法有效提高了台阶特征线提取的精度，解决了局部特征线丢失和不连续的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e87a/9371066/36721df67f46/sensors-22-05706-g019.jpg
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引用本文的文献

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