Ellipse Detection Based Bin-Picking Visual Servoing System
Kai Liu, Zengqi Sun, Masakazu Fujii · 2010
In this paper we tackle the task of picking parts from a bin (bin-picking task), employing a 6-DOF manipulator on which a single hand-eye camera is mounted. The parts are some cylinders randomly stacked in the bin. A Quasi-Random Sample Consensus (Quasi-RANSAC) ellipse detection algorithm is developed to recognize the target objects. Then the detected targets' position and posture are estimated utilizing camera's pin-hole model in conjunction with target's geometric model. After that, the target which is the easiest one to pick for the manipulator is selected from multi-detected results, and tracked while the manipulator approaches it along a collision-free path which is calculated in work space. At last, the detection accuracy and run-time performance of the Quasi-RANSAC algorithm is presented and the final position of the end-effecter is measured to describe the accuracy of the proposed bin-picking visual servoing system.