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Pose Estimation using Local Structure-Specific Shape and Appearance Context

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Authors:
  • Buch, Anders Glent ;
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    The Maersk Mc-Kinney Moller Institute, Faculty of Engineering, SDU
  • Kraft, Dirk ;
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    Orcid logo0000-0002-6125-8481
    The Maersk Mc-Kinney Moller Institute, Faculty of Engineering, SDU
  • Kämäräinen, Joni-Kristian ;
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    Tampere University of Technology
  • Petersen, Henrik Gordon ;
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    The Maersk Mc-Kinney Moller Institute, Faculty of Engineering, SDU
  • Krüger, Norbert
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    The Maersk Mc-Kinney Moller Institute, Faculty of Engineering, SDU
DOI:
10.1109/ICRA.2013.6630856
Abstract:
We address the problem of estimating the alignment pose between two models using structure-specific local descriptors. Our descriptors are generated using a combination of 2D image data and 3D contextual shape data, resulting in a set of semi-local descriptors containing rich appearance and shape information for both edge and texture structures. This is achieved by defining feature space relations which describe the neighborhood of a descriptor. By quantitative evaluations, we show that our descriptors provide high discriminative power compared to state of the art approaches. In addition, we show how to utilize this for the estimation of the alignment pose between two point sets. We present experiments both in controlled and real-life scenarios to validate our approach.
ISBN:
9781467356411
Type:
Conference paper
Language:
English
Published in:
Ieee International Conference on Robotics and Automation (icra), 2013, p. 2080-2087
Main Research Area:
Science/technology
Publication Status:
Published
Review type:
Peer Review
Conference:
2013 IEEE International Conference on Robotics and Automation, 2013
Publisher:
IEEE
Submission year:
2013
Scientific Level:
Scientific
ID:
2185995178

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