Achieving port automation of machinery at bulk terminals is a challenging problem due to the volatile operation environments and complexity of bulk loading compared to the situations in container terminals. In order to facilitate port automation, we present a method of hull modeling (reconstruction of hull’s structure) and operation target (cargo holds under loading) identification based on 3D point cloud collected by Laser Measurement System mounted on the ship loader. In the hull modeling algorithm, we incrementally register pairs of point clouds and reconstruct the 3D structure of bulk ship’s hull blocks in details through process of encoder data of the loader, FPFH feature matching and ICP algorithm. In the identification algorithm, we project real-time point clouds of the operation zone to spherical coordinate and transforms the 3D point clouds to 2D images for fast and reliable identification of operation target. Our method detects and complements four edges of the operation target through process of the 2D images and estimates both posture and size of operation target in the bulk terminal based on the complemented edges. Experimental trials show that our algorithm allows us to achieve the reconstruction of hull blocks and real-time identification of operation target with high accuracy and reliability.

与集装箱码头的情况相比，由于挥发性的操作环境和散货的复杂性，在散货码头实现机械的港口自动化是一个具有挑战性的问题。为了促进港口自动化，我们提出了一种基于船载装载机上安装的Laser Measurement System收集的3D点云的船体建模方法（船体结构的重建）和操作目标（有负载的货舱）的识别。在船体建模算法中，我们通过对装载机的编码器数据，FPFH特征匹配和ICP算法进行处理，逐步注册成对的点云对并详细重建散装船的船体块的3D结构。在识别算法中 我们将手术区域的实时点云投影到球面坐标，并将3D点云转换为2D图像，以便快速，可靠地识别手术目标。我们的方法通过对2D图像进行处理来检测和补充操作目标的四个边缘，并根据补充后的边缘估算散装终端中操作目标的姿态和大小。实验表明，该算法可以实现船体块的重构和操作目标的实时识别，具有较高的准确性和可靠性。我们的方法通过对2D图像进行处理来检测和补充操作目标的四个边缘，并根据补充后的边缘估算散装终端中操作目标的姿态和大小。实验表明，该算法可以实现船体块的重构和操作目标的实时识别，具有较高的准确性和可靠性。我们的方法通过对2D图像进行处理来检测和补充操作目标的四个边缘，并根据补充后的边缘估算散装终端中操作目标的姿态和大小。实验表明，该算法可以实现船体块的重构和操作目标的实时识别，具有较高的准确性和可靠性。