Fast graspability evaluation on single depth maps for bin picking with general grippers

Yukiyasu Domae, Yuichi Taguchi

Abstract:

We present a method that estimates graspability measures on a single depth map for grasping
objects randomly placed in a bin. Our method represents a gripper model by using two mask
images, one describing a contact region that should be filled by a target object for stable grasping,
and the other describing a collision region that should not be filled by other objects to avoid
collisions during grasping. The graspability measure is computed by convolving the mask images
with binarized depth maps, which are thresholded differently in each region according to the
minimum height of the 3D points in the region and the length of the gripper. Our method does
not assume any 3-D model of objects, thus applicable to general objects. Our representation
of the gripper model using the two mask images is also applicable to general grippers, such as
multi-finger and vacuum grippers. We apply our method to bin picking of piled objects using a
robot arm and demonstrate fast pick-and-place operations for various industrial objects.