Is Levenberg-Marquardt the Most Efficient Optimization Algorithm for Implementing Bundle Adjustment?

Authors:
Manolis I. A. Lourakis;Antonis A. Argyros
Affiliations:
Foundation for Research and Technology - Hellas;Foundation for Research and Technology - Hellas
Venue:
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Year:
2005

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Abstract

In order to obtain optimal 3D structure and viewing parameter estimates, bundle adjustment is often used as the last step of feature-based structure and motion estimation algorithms. Bundle adjustment involves the formulation of a large scale, yet sparse minimization problem, which is traditionally solved using a sparse variant of the Levenberg- Marquardt optimization algorithm that avoids storing and operating on zero entries. This paper argues that considerable computational benefits can be gained by substituting the sparse Levenberg-Marquardt algorithm in the implementation of bundle adjustment with a sparse variant of Powell驴s dog leg non-linear least squares technique. Detailed comparative experimental results provide strong evidence supporting this claim.