CAD-based Pose Estimation Design for Random Bin Picking using a RGB-D Camera

Kai-Tai Song*, Cheng Hei Wu, Sin Yi Jiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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 %.

Original languageEnglish
Pages (from-to)455-470
Number of pages16
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Issue number3-4
StatePublished - 1 Sep 2017


  • 6-DoF pose estimation
  • Industrial robot
  • Random bin picking

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