Robotic grasping method based on 3D vision for stacked rectangular objects

Robotic grasping in multi-object stacking scenes is important for autonomous robot manipulation. In this paper, we propose a 6-DoF (Degree of Freedom) grasping method for stacked rectangular objects from the single-view point clouds. We use the PointNet++ network and DBSCAN clustering algorithm to extract the target object from the whole scene. The 6-DoF pose of the gripper is obtained by our grasp pose estimation algorithm. To train the PointNet++ network, we build a small rectangular object segmentation dataset containing 800 real-world stacking scenes. The whole grasping system is lightweight, which takes about 518ms for a whole grasp planning process. Sufficient experiments show that our method gets 92% success rate and 95.5% completion rate, which satisfies the requirements of industrial applications.