  In this paper, a quick and efficient method is presented for grasping unknown
objects in clutter. The grasping method relies on real-time superquadric (SQ)
representation of partial view objects and incomplete object modelling, well
suited for unknown symmetric objects in cluttered scenarios which is followed
by optimized antipodal grasping. The incomplete object models are processed
through a mirroring algorithm that assumes symmetry to first create an
approximate complete model and then fit for SQ representation. The grasping
algorithm is designed for maximum force balance and stability, taking advantage
of the quick retrieval of dimension and surface curvature information from the
SQ parameters. The pose of the SQs with respect to the direction of gravity is
calculated and used together with the parameters of the SQs and specification
of the gripper, to select the best direction of approach and contact points.
The SQ fitting method has been tested on custom datasets containing objects in
isolation as well as in clutter. The grasping algorithm is evaluated on a PR2
and real time results are presented. Initial results indicate that though the
method is based on simplistic shape information, it outperforms other learning
based grasping algorithms that also work in clutter in terms of time-efficiency
and accuracy.
