基于地面三维激光扫描的露天矿采剥工程量计算方法

Stripping Volume Calculation Method Based on Terrestrial 3D Laser Scanning Technique

目前露天矿山采场验收测量主要采用全站仪等点式测量方法,存在采样密度低、体积计算误差大、测量周期长等不足,使用三维激光仪进行露天矿山采场验收测量已经成为一种发展趋势,然而现有的点云配准算法存在配准效率低、精度不高等问题。针对迭代最近点法(Iterative closest point,ICP)点云精配准对初始输入点云要求较高,以及原有SIFT-ICP算法需要使用影像数据作为SIFT算法粗配准数据源的问题,将原有SIFT-ICP算法进行了改进,提出了一种新SIFT-ICP算法。新算法仅使用点云一种数据源,并将SIFT算法快速识别特征点与ICP精确配准相结合,改善了精配准过程中的初始点云输入,从而实现了电云快速精确配准。辽宁省鞍山市鞍千矿现场试验表明:基于地面三维激光扫描和新SIFT-ICP算法能够快速完成点云配准,计算得到的采剥工作量相对误差仅为0.72%,可以替代目前广泛使用的全站仪等点式测量方法,并可大大提高露天矿山验收测量效率。

The total station for point measurement has been mainly adopted for the acceptance measurement of open-pit mines at present,which has the disadvantages of low sampling density,large volume calculation error and long measurement period.The use of 3D laser scanning for open-pit mine acceptance has become a trend.However,the existing point cloud registration algorithm has the problems of low registration efficiency and poor precision.Aming at the current iterative closet point(ICP)point cloud fine registration requires higher initial input point cloud,and the original SIFT-ICP(Scale invariant feature transform and iterative closest point)algorithm needs to use image data as the coarse registration data source of SIFT algorithm,the original SIFT-ICP algorithm is improved,and a new SIFT-ICP algorithm is proposed.The new algorithm uses only one data source of point cloud,and combines the SIFT algorithm to quickly identify feature points and ICP precise registration,which improves the initial point cloud input in the fine registration process,thus achieving fast and accurate registration.The actual application verification in Anqian Mine in Anshan City,Liaoning Province show that the new SIFT-ICP algorithm can quickly complete the terrestrial 3 D laser scanning point cloud registration,and the relative error between the calculated stripping volume and the reference results is only for 0.72%,which can replace the widely-used point measurement method such as total station,besides that,the efficiency of acceptance surveys in open-pit mines can be improved.