Class-specific grasping of 3D objects from a single 2D image
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Kaelbling_Class-specific.pdf
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1.08 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Chiu, Han-Pang
Liu, Huan
Kaelbling, Leslie P.
Lozano-Perez, Tomas
Date Issued
December 2010
Journal
Proceedings fo the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers
Citation
Han-Pang Chiu et al. “Class-specific grasping of 3D objects from a single 2D image.” Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on. 2010. 579-585. © Copyright 2010 IEEE
Version
Final published version
Abstract
Our goal is to grasp 3D objects given a single image, by using prior 3D shape models of object classes. The shape models, defined as a collection of oriented primitive shapes centered at fixed 3D positions, can be learned from a few labeled images for each class. The 3D class model can then be used to estimate the 3D shape of a detected object, including occluded parts, from a single image. The estimated 3D shape is used as to select one of the target grasps for the object. We show that our 3D shape estimation is sufficiently accurate for a robot to successfully grasp the object, even in situations where the part to be grasped is not visible in the input image.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS.2010.5652597
