Hole Repairing Algorithm for 3D Point Cloud Model of Symmetrical Objects Grasped by the Manipulator.

Linyan Cui,Jinshen Wang,Guolong Zhang

doi:10.3390/s21227558

Abstract

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.

Highlights

With the development of machine vision, robots can use “eyes” to obtain external information directly like human beings, which improves the intelligence and autonomy of robots
Due to the occlusion of the mechanical claw and the limited imaging angle in 3D reconstruction, a large number of holes will appear in the point cloud model of the grasped object
After the mechanical claw is removed from the 3D point cloud model, several holes with a regular shape and medium size will be left on the side of the point cloud, whose shape is roughly equivalent to the contour of the mechanical claw

Summary

Introduction

With the development of machine vision, robots can use “eyes” to obtain external information directly like human beings, which improves the intelligence and autonomy of robots. The eye-to-hand camera can model the grasped object and the surrounding environment with 3D reconstruction algorithms. Due to the occlusion of the mechanical claw and the limited imaging angle in 3D reconstruction, a large number of holes will appear in the point cloud model of the grasped object. The occlusion of the mechanical claw comes from the fact that the mechanical claw and the object are modeled together in the 3D reconstruction. Due to the limited imaging angle in 3D reconstruction, the top of the object cannot be imaged when the manipulator grabs the object, resulting in a missing surface at the top of the point cloud. The missing surface can be regarded as a large area hole, and it exhibits different characteristics from the holes caused by the occlusion of the mechanical claw

Methods

Results

Conclusion