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Branch-and-Bound Methods for Euclidean Registration Problems
May 2009 (vol. 31 no. 5)
pp. 783-794
ASCII Text
x 
 
Carl Olsson, Fredrik Kahl, Magnus Oskarsson, "Branch-and-Bound Methods for Euclidean Registration Problems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp. 783-794, May, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2008.131,
author = {Carl Olsson and Fredrik Kahl and Magnus Oskarsson},
title = {Branch-and-Bound Methods for Euclidean Registration Problems},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {31},
number = {5},
issn = {0162-8828},
year = {2009},
pages = {783-794},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.131},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Branch-and-Bound Methods for Euclidean Registration Problems
IS - 5
SN - 0162-8828
SP783
EP794
EPD - 783-794
A1 - Carl Olsson,
A1 - Fredrik Kahl,
A1 - Magnus Oskarsson,
PY - 2009
KW - Registration
KW - camera pose
KW - global optimization
KW - branch-and-bound.
VL - 31
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Carl Olsson, Lund University, Lund
Fredrik Kahl, Lund University, Lund
Magnus Oskarsson, Lund University, Lund
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.

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Index Terms:
Registration, camera pose, global optimization, branch-and-bound.
Citation:
Carl Olsson, Fredrik Kahl, Magnus Oskarsson, "Branch-and-Bound Methods for Euclidean Registration Problems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp. 783-794, May 2009, doi:10.1109/TPAMI.2008.131
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