Random Bin Picking with Multi-view Image Acquisition and CAD-Based Pose Estimation

Abstract

Due to the recent development of industrial automation, using vision based techniques to estimate the pose of workpieces for random bin picking application is a future trend. The common method for 3D pose estimation is to use the CAD model and feature points. However, the CAD-based method has a high degree of flexibility in the assembly line, and it is not easy to acquire all necessary features of the workpieces. In this work, we use two depth cameras to capture the 3D scene, and propose a CAD-based multi-view pose estimation algorithm. First, RANSAC and an outlier filter are adopted for noise removal and object segmentation. We use a voting scheme for preliminary pose estimation, followed by the ICP algorithm to derive a more precise target pose. Finally, with disturbance detection, the robot arm can grip the objects without rescanning for each operation. A complete system for 3D scene acquisition using structured light cameras, 3D pose estimation and robot arm control is developed for the pick-and-place task. Experiments are carried out in the real scene environment to demonstrate the feasibility of the proposed technique.