Pose Estimation Focusing on One Object Based on Grasping Quality in Bin Picking

Labor shortages are becoming a significant issue in manufacturing sites, and automation of bin picking is required. To automate bin picking, it is necessary to estimate the pose of objects. Traditionally, the poses of multiple objects are estimated. However, estimating the poses of multiple objects is difficult, because it requires accurate pose estimation even for objects that are overlapped with others. This study is based on the fact that a robot can only grasp one object at a time. We developed a method that selects an easily graspable object first and focuses on pose estimation for this single object. In the Siléane dataset, the accuracy of pose estimation was 86.1%, an improvement of 17.5 points compared with the conventional method, PPR-Net.