基于无人机影像的露天矿工程量监测分析方法

提出了一种基于无人机影像序列的露天矿工程量(采剥量、堆放量等)计算方法.该方法利用旋翼无人机搭载低成本便携式数码摄像机获取露天矿山不同时间的视频帧或影像序列.基于运动恢复结构(Sf M)和多目立体视觉(PMVS)算法,自动生成矿山完整、致密的三维点云.研究设计了一种基于形态不变区的点云配准方法进行两期点云空间配准,并采用DTM三角网差值法计算矿山工程量.矿堆体积变化无人机监测实验结果表明,该方法重建点云模型的点间相对误差小于±1%,堆放体积变化监测精度接近92%,基本达到地面Li DAR扫描的堆放体积变化监测精度.

Method of Engineering Volume Monitoring and Calculation for Open-Pit Mine from UAV Images

The image sequences from an unmanned aerial vehicle (UAV) are used to calculate the engineering volume (overburden amount， stacking amount， etc.) of open-pit mine. Firstly， two sets of video frames or optical images of the open-pit mine are collected with a time interval using a portable digital camera installed on an octocopter. Next， two groups of the point clouds are automatically generated by implementing structure from motion (SfM) and patch-based multi-view stereo (PMVS) algorithms. And then， the two point clouds are fine registered with a constant region-based registration method. Finally， the engineering volume is computed with a differential method for digital terrain model triangulated irregular network (DTM-TIN). It shows that the relative error of the point cloud model is lower than ±1% in the experiment for change detection of a stacking stockpile with UAV images. Moreover， the accuracy for monitoring the volume change is up to 92%， which is comparable to that of a terrestrial laser scanning.