Probabilistic Models of Object Geometry with Application to Grasping
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Roy_Probabilistic models.pdf
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Author(s)
Rus, Daniela L.
Glover, Jared
Roy, Nicholas
Date Issued
August 2009
Journal
International Journal of Robotics Research
Publisher
Sage Publications
Citation
Glover, Jared, Daniela Rus, and Nicholas Roy. “Probabilistic Models of Object Geometry with Application to Grasping.” The International Journal of Robotics Research 28.8 (2009): 999 -1019.
Version
Author's final manuscript
Abstract
Robot manipulators typically rely on complete
knowledge of object geometry in order to plan motions and
compute grasps. But when an object is not fully in view it can
be difficult to form an accurate estimate of the object’s shape
and pose, particularly when the object deforms.
In this paper we describe a generative model of object geometry
based on Mardia and Dryden’s “Probabilistic Procrustean
Shape” which captures both non-rigid deformations and object
variability in a class. We extend their shape model to the
setting where point correspondences are unknown using Scott
and Nowak’s COPAP framework. We use this model to recognize
objects in a cluttered image and to infer their complete 2-D
boundaries with a novel algorithm called OSIRIS. We show
examples of learned models from image data and demonstrate
how the models can be used by a manipulation planner to grasp
objects in cluttered visual scenes.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Attribution-Noncommercial-Share Alike 3.0 Unported
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DOI of Published Version
https://doi.org/10.1177/0278364909340332
