A fast volume measurement method for obtaining point cloud data from bulk stockpiles
Weili Ding, Kai Zhang, Changyu Shao
Abstract
Abstract To improve the efficiency of port bulk handling, a fast volume measurement algorithm for irregular bulk cargo is proposed in this paper. The elevation laser scanner and solid-state Lidar are used to determine the geometric information of bulk piles. The 3D point cloud data of the irregular bulk cargo was extracted, and the volume of the pile was calculated using the point cloud. To realize fast measurements, the algorithm first obtains a series of sliced point clouds and generates the slice matrix via dimensionality reduction and rasterization. Next, the area of the slice matrix is filled by the X-scan line algorithm. Finally, the volume of the whole point clouds is obtained by integrating the area of each slice matrix. Extensive experiments on datasets of realistic scenarios demonstrate that the proposed measurement method can complete point cloud reconstruction and volume calculation for different types of stockpiles with a good balance of accuracy, robustness, and execution efficiency.