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Bundle Adjustment using Conjugate Gradients with Multiscale Preconditioning

Byröd, Martin LU and Åström, Karl LU (2009) British Machine Vision Conference, 2009 In British Machine Vision Conference
Abstract
Bundle adjustment is a key component of almost any feature based 3D reconstruction

system, used to compute accurate estimates of calibration parameters and structure and

motion configurations. These problems tend to be very large, often involving thousands

of variables. Thus, efficient optimization methods are crucial. The traditional Levenberg

Marquardt algorithm with a direct sparse solver can be efficiently adapted to the special

structure of the problem and works well for small to medium size setups. However, for

larger scale configurations the cubic computational complexity makes this approach pro-

hibitively expensive. The natural step here is to turn to iterative methods for... (More)
Bundle adjustment is a key component of almost any feature based 3D reconstruction

system, used to compute accurate estimates of calibration parameters and structure and

motion configurations. These problems tend to be very large, often involving thousands

of variables. Thus, efficient optimization methods are crucial. The traditional Levenberg

Marquardt algorithm with a direct sparse solver can be efficiently adapted to the special

structure of the problem and works well for small to medium size setups. However, for

larger scale configurations the cubic computational complexity makes this approach pro-

hibitively expensive. The natural step here is to turn to iterative methods for solving the

normal equations such as conjugate gradients. So far, there has been little progress in this

direction. This is probably due to the lack of suitable pre-conditioners, which are con-

sidered essential for the success of any iterative linear solver. In this paper, we show how

multi scale representations, derived from the underlying geometric layout of the problem,

can be used to dramatically increase the power of straight forward preconditioners such

as Gauss-Seidel. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Computer vision, non-linear least squares problems, simultaneous localization and mapping, bundle adjustment
in
British Machine Vision Conference
pages
10 pages
conference name
British Machine Vision Conference, 2009
external identifiers
  • Scopus:84898925190
language
English
LU publication?
yes
id
16c6e967-f1b0-4461-bd6e-f502dae55a98 (old id 1612231)
alternative location
http://www.bmva.org/bmvc/2009/index.htm
date added to LUP
2010-06-08 12:09:26
date last changed
2016-10-13 04:53:03
@misc{16c6e967-f1b0-4461-bd6e-f502dae55a98,
  abstract     = {Bundle adjustment is a key component of almost any feature based 3D reconstruction<br/><br>
system, used to compute accurate estimates of calibration parameters and structure and<br/><br>
motion configurations. These problems tend to be very large, often involving thousands<br/><br>
of variables. Thus, efficient optimization methods are crucial. The traditional Levenberg<br/><br>
Marquardt algorithm with a direct sparse solver can be efficiently adapted to the special<br/><br>
structure of the problem and works well for small to medium size setups. However, for<br/><br>
larger scale configurations the cubic computational complexity makes this approach pro-<br/><br>
hibitively expensive. The natural step here is to turn to iterative methods for solving the<br/><br>
normal equations such as conjugate gradients. So far, there has been little progress in this<br/><br>
direction. This is probably due to the lack of suitable pre-conditioners, which are con-<br/><br>
sidered essential for the success of any iterative linear solver. In this paper, we show how<br/><br>
multi scale representations, derived from the underlying geometric layout of the problem,<br/><br>
can be used to dramatically increase the power of straight forward preconditioners such<br/><br>
as Gauss-Seidel.},
  author       = {Byröd, Martin and Åström, Karl},
  keyword      = {Computer vision,non-linear least squares problems,simultaneous localization and mapping,bundle adjustment},
  language     = {eng},
  pages        = {10},
  series       = {British Machine Vision Conference},
  title        = {Bundle Adjustment using Conjugate Gradients with Multiscale Preconditioning},
  year         = {2009},
}