堆石坝检测坑点云切片分割提取方法

Point cloud extraction of rockfill dam test pits based on slice segmentation

堆石坝压实质量的检测是大坝工程安全稳定运行的重要保障措施之一。传统的灌水法中检测坑体积的测量耗时耗力,利用三维激光扫描技术可以快速计算检测坑体积,提高压实质量检测效率,而从扫描点云数据精确提取出检测坑点云数据是确保该方法计算精度的关键环节。本文提出了一种基于点云切片分割的堆石坝检测坑点云提取方法:首先对点云数据进行切片处理,然后采用单向搜索排序方法对每个切片点集进行点排序,最后采用一种基于切片点集排序序号的聚类分割方法分割每个点云切片,从而精确提取出检测坑表面点云。利用MATLAB编程实现了基于点云切片分割的堆石坝检测坑点云的快速提取,并通过堆石坝检测坑点云数据开展试验,验证了该方法的有效性。

Detection of compaction quality during rockfill dam construction is an important safeguard for its safe and stable operation,but the traditional irrigation method relies on measuring the detection pit volume that is time-consuming and labor-intensive.Today,the three-dimensional laser scanning technology can be adopted to calculate the detection pit volume quickly and improve the efficiency of compaction quality detection.To ensure the calculation accuracy of this new method,the key link is accurate extraction of the detection pit point cloud data from the scanned data.This paper develops a point cloud extraction method based on point cloud slicing that is able to accurately extract the point cloud on the surface of the inspection pit.First,the point cloud data is sliced;then a one-way search sorting method is used to sort the points of each sliced point set;finally,a cluster segmentation method based on the sorting number of the sliced point set is adopted to segment each slice.We have designed a MATLAB code to realize rapid extraction of the point cloud for a rockfill dam inspection pit based on this new method,and tested it using the point cloud data to verify its effectiveness and accuracy.