Paper Info
Title
Grasping Unknown Objects in Clutter by Superquadric Representation
Abstract
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 robot 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.
Year
DOI
Venue
2018
10.1109/IRC.2018.00062
2018 Second IEEE International Conference on Robotic Computing (IRC)
Keywords
DocType
Volume
Grasping,Unknown Objects,Superquadric
Conference
abs/1710.02121
ISBN
Citations 
PageRank 
978-1-5386-4653-3
0
0.34
References 
Authors
18
3
Name
Order
Citations
PageRank
Abhijit Makhal140.80
Thomas, F.2265.36
Alba Perez-Gracia301.35