Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/54527
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Type: Conference paper
Title: Solving quadratically constrained geometrical problems using Lagrangian duality
Author: Olsson, C.
Eriksson, A.
Citation: Proceedings of the 19th International Conference on Pattern Recognition, 2008;. pp.1-5
Publisher: IEEE
Publisher Place: CD
Issue Date: 2008
ISBN: 9781424421749
ISSN: 1051-4651
Conference Name: International Conference on Pattern Recognition (19th : 2008 : Florida)
Statement of
Responsibility: 
Carl Olsson & Anders Eriksson
Abstract: In this paper we consider the problem of solving different pose and registration problems under rotational constraints. Traditionally, methods such as the iterative closest point algorithm have been used to solve these problems. They may however get stuck in local minima due to the non-convexity of the problem. In recent years methods for finding the global optimum, based on Branch and Bound and convex under-estimators, have been developed. These methods are provably optimal, however since they are based on global optimization methods they are in general more time consuming than local methods. In this paper we adopt a dual approach. Rather than trying to find the globally optimal solution we investigate the quality of the solutions obtained using Lagrange duality. Our approach allows us to formulate a single convex semidefinite program that approximates the original problem well.
DOI: 10.1109/ICPR.2008.4761896
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Computer Science publications

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