標題: | CAD-based Pose Estimation Design for Random Bin Picking using a RGB-D Camera |
作者: | Song, Kai-Tai Wu, Cheng-Hei Jiang, Sin-Yi 資訊工程學系 Department of Computer Science |
關鍵字: | Industrial robot;Random bin picking;6-DoF pose estimation |
公開日期: | 1-九月-2017 |
摘要: | This paper presents a CAD-based six-degrees-of-freedom (6-DoF) pose estimation design for random bin picking for multiple objects. A virtual camera generates a point cloud database for the objects using their 3D CAD models. To reduce the computational time of 3D pose estimation, a voxel grid filter reduces the number of points for the 3D cloud of the objects. A voting scheme is used for object recognition and to estimate the 6-DoF pose for different objects. An outlier filter filters out badly matching poses so that the robot arm always picks up the upper object in the bin, which increases the success rate. In a computer simulation using a synthetic scene, the average recognition rate is 97.81 % for three different objects with various poses. A series of experiments have been conducted to validate the proposed method using a Kuka robot arm. The average recognition rate for three objects is 92.39 % and the picking success rate is 89.67 %. |
URI: | http://dx.doi.org/10.1007/s10846-017-0501-1 http://hdl.handle.net/11536/145875 |
ISSN: | 0921-0296 |
DOI: | 10.1007/s10846-017-0501-1 |
期刊: | JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS |
Volume: | 87 |
起始頁: | 455 |
結束頁: | 470 |
顯示於類別: | 期刊論文 |