Branch-and-Bound Methods for Euclidean Registration Problems.
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1367607
- author
- Olsson, Carl
LU
; Kahl, Fredrik
LU
and Oskarsson, Magnus
LU
- organization
- publishing date
- 2009
- 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
- pmid:19299855
- 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
- 2016-04-01 12:52:39
- date last changed
- 2022-03-21 07:16:20
@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}}, doi = {{10.1109/TPAMI.2008.131}}, volume = {{31}}, year = {{2009}}, }