Resection-Intersection Bundle Adjustment Revisited. (12th December 2013)

• Record Type: Journal Article
• Authors: Lakemond, Ruan; Fookes, Clinton; Sridharan, Sridha
• Other Names: Gasteratos A. Academic Editor.; Pardàs M. Academic Editor.
• Is Part Of: ISRN machine vision. Volume 2013(2013)
• Publication Date: 2013-12-12
• Subjects: Computer vision -- Periodicals; Computer vision; Periodicals; Electronic journals; 006.37
• DOI: 10.1155/2013/261956
• Languages: English
• ISSNs: 2090-7796

Abstract

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.