Robotic Grasping With Multi-View Image Acquisition and Model-Based Pose Estimation

Abstract

Due to recent advances on hardware and software technologies, industrial automation has been significantly improved in the past few decades. For random bin picking applications, it is a future trend to use machine vision based approaches to estimate the 3D poses of workpieces. In this work, we present a robotic grasping system with multi-view depth image acquisition. First, RANSAC and an outlier filter are adopted for noise removal and multi-object segmentation. A voting scheme is then used for preliminary pose computation, followed by the ICP algorithm to derive a more precise target orientation. A model-based registration approach using a genetic algorithm with parameter minimization is proposed for 6-DOF pose estimation. Finally, the grasping efficiency is increased by disturbance detection, which reduces the number of 3D data scanning for multiple operations. The experiments are carried out in the real scene environment, and the performance evaluation has demonstrated the feasibility of the proposed technique.