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**Harvard**

Fredriksson, J., Larsson, V., Olsson, C., Enqvist, O. och Kahl, F. (2016) *Efficient algorithms for robust estimation of relative translation*.

** BibTeX **

@article{

Fredriksson2016,

author={Fredriksson, Johan and Larsson, Viktor and Olsson, Carl and Enqvist, Olof and Kahl, Fredrik},

title={Efficient algorithms for robust estimation of relative translation},

journal={Image and Vision Computing},

issn={02628856},

volume={52},

pages={114},

abstract={One of the key challenges for structure from motion systems in order to make them robust to failure is the ability to handle outliers among the correspondences. In this paper we present two new algorithms that find the optimal solution in the presence of outliers when the camera undergoes a pure translation. The first algorithm has polynomial-time computational complexity, independently of the amount of outliers. The second algorithm does not offer such a theoretical complexity guarantee, but we demonstrate that it is magnitudes faster in practice. No random sampling approaches such as RANSAC are guaranteed to find an optimal solution, while our two methods do. We evaluate and compare the algorithms both on synthetic and real experiments. We also embed the algorithms in a larger system, where we optimize for the rotation angle as well (the rotation axis is measured by other means). The experiments show that for problems with a large amount of outliers, the RANSAC estimates may deteriorate compared to our optimal methods.},

year={2016},

keywords={Branch and bound; Epipolar geometry; Structure from motion; Two-view geometry},

}

** RefWorks **

RT Journal Article

SR Electronic

ID 238963

A1 Fredriksson, Johan

A1 Larsson, Viktor

A1 Olsson, Carl

A1 Enqvist, Olof

A1 Kahl, Fredrik

T1 Efficient algorithms for robust estimation of relative translation

YR 2016

JF Image and Vision Computing

SN 02628856

VO 52

AB One of the key challenges for structure from motion systems in order to make them robust to failure is the ability to handle outliers among the correspondences. In this paper we present two new algorithms that find the optimal solution in the presence of outliers when the camera undergoes a pure translation. The first algorithm has polynomial-time computational complexity, independently of the amount of outliers. The second algorithm does not offer such a theoretical complexity guarantee, but we demonstrate that it is magnitudes faster in practice. No random sampling approaches such as RANSAC are guaranteed to find an optimal solution, while our two methods do. We evaluate and compare the algorithms both on synthetic and real experiments. We also embed the algorithms in a larger system, where we optimize for the rotation angle as well (the rotation axis is measured by other means). The experiments show that for problems with a large amount of outliers, the RANSAC estimates may deteriorate compared to our optimal methods.

LA eng

DO 10.1016/j.imavis.2016.05.011

LK http://dx.doi.org/10.1016/j.imavis.2016.05.011

OL 30