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The registration problem revisited: Optimal solutions from points, lines and planes

Olsson, Carl LU ; Kahl, Fredrik LU and Oskarsson, Magnus LU (2006) 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 In Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 1. p.1206-1213
Abstract
In this paper we propose a practical and efficient method for finding the globally optimal solution to the problem of pose estimation of a known object. We present a framework that allows us to use both point-to-point, point-to-line and point-to-plane correspondences in the optimization algorithm. Traditional methods such as the iterative closest point algorithm may get trapped in local minima due to the non-convexity of the problem, however, our approach guarantees global optimality. The approach is based on ideas from global optimization theory, in particular, convex under-estimators in combination with branch and bound. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Under-estimators, Non-convexity, Point-to-line, Point-to-plane
in
Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
volume
1
pages
1206 - 1213
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
external identifiers
  • Scopus:33845572537
DOI
10.1109/CVPR.2006.307
language
English
LU publication?
yes
id
afaceff1-a2e1-4c07-9b0c-19d14e9032dd (old id 616690)
date added to LUP
2007-11-24 12:07:58
date last changed
2016-10-13 04:39:34
@misc{afaceff1-a2e1-4c07-9b0c-19d14e9032dd,
  abstract     = {In this paper we propose a practical and efficient method for finding the globally optimal solution to the problem of pose estimation of a known object. We present a framework that allows us to use both point-to-point, point-to-line and point-to-plane correspondences in the optimization algorithm. Traditional methods such as the iterative closest point algorithm may get trapped in local minima due to the non-convexity of the problem, however, our approach guarantees global optimality. The approach is based on ideas from global optimization theory, in particular, convex under-estimators in combination with branch and bound. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data.},
  author       = {Olsson, Carl and Kahl, Fredrik and Oskarsson, Magnus},
  keyword      = {Under-estimators,Non-convexity,Point-to-line,Point-to-plane},
  language     = {eng},
  pages        = {1206--1213},
  publisher    = {ARRAY(0xb673520)},
  series       = {Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006},
  title        = {The registration problem revisited: Optimal solutions from points, lines and planes},
  url          = {http://dx.doi.org/10.1109/CVPR.2006.307},
  volume       = {1},
  year         = {2006},
}