The registration problem revisited: Optimal solutions from points, lines and planes
(2006) 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:
https://lup.lub.lu.se/record/616690
- author
- Olsson, Carl LU ; Kahl, Fredrik LU and Oskarsson, Magnus LU
- organization
- publishing date
- 2006
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Under-estimators, Non-convexity, Point-to-line, Point-to-plane
- host publication
- 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
- conference location
- New York, NY, United States
- conference dates
- 2006-06-17 - 2006-06-22
- 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
- 2016-04-04 10:21:28
- date last changed
- 2022-02-13 19:35:00
@inproceedings{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}}, booktitle = {{Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006}}, keywords = {{Under-estimators; Non-convexity; Point-to-line; Point-to-plane}}, language = {{eng}}, pages = {{1206--1213}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{The registration problem revisited: Optimal solutions from points, lines and planes}}, url = {{http://dx.doi.org/10.1109/CVPR.2006.307}}, doi = {{10.1109/CVPR.2006.307}}, volume = {{1}}, year = {{2006}}, }