密集匹配点云下的矿石块度提取

在矿山爆破作业中，评判矿山爆破质量的重要指标是爆破后矿石块度的大小。矿石块度的大小直接影响着后期的生产作业。针对露天矿施工过程中矿石块度测定问题，提出了一种基于密集匹配点云数据的矿石块度测定方法。首先通过寻找矿石堆中最高点与最低点计算不同矿石堆的坡度；然后依据计算得到的矿石堆坡度信息以及点云灰度信息、邻域特征等，采用粗提取与精提取相结合的方式对矿石点云边界线进行提取；依据提取出的边界线实现不同矿石点云的有效分割；最后利用分割得到的单个矿石点云进行块度估算以及矿石分布规律研究。试验表明本文方法可以有效的分割出单个矿石点云，且分割效果较好，研究区内矿石块度大多介于0.5-1.0m。

Extraction of Ore Lumpiness under Dense Matching Point Cloud

In mine blasting operation, the important index to evaluate the quality of mine blasting is the size of ore fragmentation after blasting. The size of ore lumpiness directly affects the later production operation. Aimed at the problem of ore fragmentation determination during the construction of open-pit mines, a method of ore fragmentation determination based on densely matched point cloud data is proposed. First, the highest point and the lowest point in the ore pile are found to calculate the slope of different ore piles; then according to the calculated slope information of the ore pile, point cloud gray information, neighborhood features, etc., a combination of rough extraction and fine extraction is used to perform the calculation; the extracted boundary lines are used as the data of realize the effective segmentation of different ore point clouds; finally, the segmented single ore point cloud is used to estimate the fragmentation and study the ore distribution law. Experiments show that the proposed method can effectively segment a single ore point cloud, and the segmentation effect is good, and the bulk of the ore in the study area is between 0.5-1.0m.