Cui Linyan, Zhang Guolong, Wang Jinshen
Image Processing Center, School of Astronautics, Beihang University, Beijing 102206, China.
Sensors (Basel). 2021 Nov 13;21(22):7558. doi: 10.3390/s21227558.
For the engineering application of manipulator grasping objects, mechanical arm occlusion and limited imaging angle produce various holes in the reconstructed 3D point clouds of objects. Acquiring a complete point cloud model of the grasped object plays a very important role in the subsequent task planning of the manipulator. This paper proposes a method with which to automatically detect and repair the holes in the 3D point cloud model of symmetrical objects grasped by the manipulator. With the established virtual camera coordinate system and boundary detection, repair and classification of holes, the closed boundaries for the nested holes were detected and classified into two kinds, which correspond to the mechanical claw holes caused by mechanical arm occlusion and the missing surface produced by limited imaging angle. These two kinds of holes were repaired based on surface reconstruction and object symmetry. Experiments on simulated and real point cloud models demonstrate that our approach outperforms the other state-of-the-art 3D point cloud hole repair algorithms.
对于机械手抓取物体的工程应用，机械臂遮挡和有限的成像角度会在物体重建的三维点云中产生各种孔洞。获取被抓取物体的完整点云模型对机械手后续的任务规划起着非常重要的作用。本文提出了一种自动检测和修复机械手抓取的对称物体三维点云模型中孔洞的方法。通过建立虚拟相机坐标系以及孔洞的边界检测、修复和分类，检测出嵌套孔洞的封闭边界并将其分为两类，分别对应机械臂遮挡导致的机械爪孔洞和有限成像角度产生的缺失表面。基于表面重建和物体对称性对这两类孔洞进行修复。在模拟和真实点云模型上的实验表明，我们的方法优于其他先进的三维点云孔洞修复算法。
