Title:
Perceiving Clutter and Surfaces for Object Placement in Indoor Environments

dc.contributor.author Schuster, Martin J. en_US
dc.contributor.author Okerman, Jason en_US
dc.contributor.author Nguyen, Hai en_US
dc.contributor.author Rehg, James M. en_US
dc.contributor.author Kemp, Charles C. en_US
dc.contributor.corporatename Georgia Institute of Technology. Healthcare Robotics Lab en_US
dc.contributor.corporatename Technische Universität München en_US
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines en_US
dc.date.accessioned 2011-03-04T16:05:56Z
dc.date.available 2011-03-04T16:05:56Z
dc.date.issued 2010-12
dc.description ©2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. en_US
dc.description Presented at the 2010 IEEE-RAS International Conference on Humanoid Robots, Nashville, TN, USA, December 6-8, 2010. en_US
dc.description.abstract Handheld manipulable objects can often be found on flat surfaces within human environments. Researchers have previously demonstrated that perceptually segmenting a flat surface from the objects resting on it can enable robots to pick and place objects. However, methods for performing this segmentation can fail when applied to scenes with natural clutter. For example, low-profile objects and dense clutter that obscures the underlying surface can complicate the interpretation of the scene. As a first step towards characterizing the statistics of real-world clutter in human environments, we have collected and hand labeled 104 scans of cluttered tables using a tilting laser range finder (LIDAR) and a camera. Within this paper, we describe our method of data collection, present notable statistics from the dataset, and introduce a perceptual algorithm that uses machine learning to discriminate surface from clutter. We also present a method that enables a humanoid robot to place objects on uncluttered parts of flat surfaces using this perceptual algorithm. In cross-validation tests, the perceptual algorithm achieved a correct classification rate of 78.70% for surface and 90.66% for clutter, and outperformed our previously published algorithm. Our humanoid robot succeeded in 16 out of 20 object placing trials on 9 different unaltered tables, and performed successfully in several high-clutter situations. 3 out of 4 failures resulted from placing objects too close to the edge of the table. en_US
dc.identifier.citation Martin J. Schuster, Jason Okerman, Hai Nguyen, James M. Rehg, and Charles C. Kemp,"Perceiving Clutter and Surfaces for Object Placement in Indoor Environments," Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots, 2010. en_US
dc.identifier.isbn 978-1-4244-8689-2
dc.identifier.isbn 978-1-4244-8688-5
dc.identifier.uri http://hdl.handle.net/1853/37072
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Institute of Electrical and Electronics Engineers en_US
dc.subject Cameras en_US
dc.subject Humanoid robots en_US
dc.subject Image segmentation en_US
dc.subject Laser ranging en_US
dc.subject Learning (artificial intelligence) en_US
dc.subject Manipulators en_US
dc.subject Optical radar en_US
dc.subject Radar clutter en_US
dc.subject Robot vision en_US
dc.title Perceiving Clutter and Surfaces for Object Placement in Indoor Environments en_US
dc.type Text
dc.type.genre Proceedings
dc.type.genre Post-print
dspace.entity.type Publication
local.contributor.author Rehg, James M.
local.contributor.author Kemp, Charles C.
local.contributor.corporatename Healthcare Robotics Lab
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
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