6DOF Object Positioning and Grasping Approach for Industrial Robots Based on Boundary Point Cloud Features

Abstract

For the three-dimensional (3D) pose estimation of metal blank casts estimate in industrial production process, a novel 6-degree-of-freedom (6DOF) positioning and grasping approach for industrial robots based on boundary features and combining structured light 3D vision and the point cloud matching method is proposed. The proposed approach first uses the Gray code plus grating phase-shift algorithm to reconstruct the 3D surface information of the object. Subsequently, an improved point pair feature (PPF) matching location method based on point cloud boundary extraction is proposed to realize the accurate 6DOF pose estimation of the object. In this method, the point cloud boundary feature is used to optimize the PPF algorithm. Finally, by combining with the point cloud preprocessing process and the improved PPF matching location method, the 6DOF pose positioning of metal blank casting by industrial robots is realized. The proposed approach can accurately complete pose measurement and positioning of objects placed randomly in an open environment. The experimental results demonstrate that the proposed PPF matching location method significantly improves both matching speed and accuracy compared to the traditional PPF algorithm.