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Branch-and-Bound Methods for Euclidean Registration Problems.

Olsson, Carl LU ; Kahl, Fredrik LU and Oskarsson, Magnus LU (2009) In IEEE Transactions on Pattern Analysis and Machine Intelligence 31(5). p.783-794
Abstract
In this paper, we propose a practical and efficient method for finding the globally optimal solution to the problem of determining the pose of an object. We present a framework that allows us to use point-to-point, point-to-line, and point-to-plane correspondences for solving various types of pose and registration problems involving euclidean (or similarity) transformations. Traditional methods such as the iterative closest point algorithm or bundle adjustment methods for camera pose may get trapped in local minima due to the nonconvexity of the corresponding optimization problem. Our approach of solving the mathematical optimization problems guarantees global optimality. The optimization scheme is based on ideas from global optimization... (More)
In this paper, we propose a practical and efficient method for finding the globally optimal solution to the problem of determining the pose of an object. We present a framework that allows us to use point-to-point, point-to-line, and point-to-plane correspondences for solving various types of pose and registration problems involving euclidean (or similarity) transformations. Traditional methods such as the iterative closest point algorithm or bundle adjustment methods for camera pose may get trapped in local minima due to the nonconvexity of the corresponding optimization problem. Our approach of solving the mathematical optimization problems guarantees global optimality. The optimization scheme is based on ideas from global optimization theory, in particular convex underestimators in combination with branch-and-bound methods. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data. We also give examples of where traditional methods fail due to the local minima problem. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
IEEE Transactions on Pattern Analysis and Machine Intelligence
volume
31
issue
5
pages
783 - 794
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000264144500002
  • pmid:19299855
  • scopus:64849097743
ISSN
1939-3539
DOI
10.1109/TPAMI.2008.131
language
English
LU publication?
yes
id
84eaed03-93e9-470d-99a9-8837632deff3 (old id 1367607)
date added to LUP
2009-04-07 08:10:06
date last changed
2017-09-03 03:58:55
@article{84eaed03-93e9-470d-99a9-8837632deff3,
  abstract     = {In this paper, we propose a practical and efficient method for finding the globally optimal solution to the problem of determining the pose of an object. We present a framework that allows us to use point-to-point, point-to-line, and point-to-plane correspondences for solving various types of pose and registration problems involving euclidean (or similarity) transformations. Traditional methods such as the iterative closest point algorithm or bundle adjustment methods for camera pose may get trapped in local minima due to the nonconvexity of the corresponding optimization problem. Our approach of solving the mathematical optimization problems guarantees global optimality. The optimization scheme is based on ideas from global optimization theory, in particular convex underestimators in combination with branch-and-bound methods. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data. We also give examples of where traditional methods fail due to the local minima problem.},
  author       = {Olsson, Carl and Kahl, Fredrik and Oskarsson, Magnus},
  issn         = {1939-3539},
  language     = {eng},
  number       = {5},
  pages        = {783--794},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title        = {Branch-and-Bound Methods for Euclidean Registration Problems.},
  url          = {http://dx.doi.org/10.1109/TPAMI.2008.131},
  volume       = {31},
  year         = {2009},
}