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Paper TuC09.1

Pinies, Pedro (Universidad de Zaragoza), Paz, Lina Marķa (University of Zaragoza), Haner, Sebastian (Lund Institute of Technology), Heyden, Anders (Lund Institute of Technology)

Decomposable Bundle Adjustment Using a Junction Tree

Scheduled for presentation during the Regular Session "Mapping" (TuC09), Tuesday, May 15, 2012, 14:30−14:45, Meeting Room 9 (Sa)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 13, 2017

Keywords Computer Vision for Robotics and Automation, Mapping, Localization

Abstract

The Sparse Bundle Adjustment (SBA) algorithm is a widely used method to solve multi-view reconstruction problems in vision. The critical cost of SBA depends on the fill in of the reduced camera matrix whose pattern is known as the Secondary structure of the problem. In centered object applications where a large number of images are taken in a small area the camera matrix obtained when points are eliminated is dense. On the contrary, visual mapping systems where long trajectories are traversed yield sparse matrices. In this paper, we propose a Decomposable Bundle Adjustment (DBA) method which naturally adapts to the fill in pattern of the camera matrix improving the performance on visual mapping systems. The proposed algorithm is able to decompose the normal equations into small subsystems which are ordered in a junction tree structure. To solve the original system, local factorizations of the small dense matrices are passed between clusters in the tree. The DBA algorithm has been tested for simulated and real data experiments for different environment configurations showing good performance.

 

 

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