Table of Contents
ISRN Machine Vision
Volume 2013 (2013), Article ID 261956, 8 pages
http://dx.doi.org/10.1155/2013/261956
Research Article

Resection-Intersection Bundle Adjustment Revisited

Image and Video Research Laboratory, Queensland University of Technology, GPO Box 2434, 2 George Street, Brisbane, QLD 4001, Australia

Received 17 August 2013; Accepted 18 November 2013

Academic Editors: A. Gasteratos and M. Pardàs

Copyright © 2013 Ruan Lakemond et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Bundle adjustment is one of the essential components of the computer vision toolbox. This paper revisits the resection-intersection approach, which has previously been shown to have inferior convergence properties. Modifications are proposed that greatly improve the performance of this method, resulting in a fast and accurate approach. Firstly, a linear triangulation step is added to the intersection stage, yielding higher accuracy and improved convergence rate. Secondly, the effect of parameter updates is tracked in order to reduce wasteful computation; only variables coupled to significantly changing variables are updated. This leads to significant improvements in computation time, at the cost of a small, controllable increase in error. Loop closures are handled effectively without the need for additional network modelling. The proposed approach is shown experimentally to yield comparable accuracy to a full sparse bundle adjustment (20% error increase) while computation time scales much better with the number of variables. Experiments on a progressive reconstruction system show the proposed method to be more efficient by a factor of 65 to 177, and 4.5 times more accurate (increasing over time) than a localised sparse bundle adjustment approach.