Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/88027
Type: Conference paper
Title: Adaptive relative bundle adjustment
Author: Sibley, G.
Mei, C.
Reid, I.
Newman, P.
Citation: Proceedings of Robotics: Science and Systems V, 2010 / Trinkle, J., Matsuoka, Y., Castellanos, J. (ed./s), vol.5, pp.1-8
Publisher: MIT Press
Publisher Place: Cambridge, Massachusetts
Issue Date: 2010
ISBN: 026251463X
9780262514637
ISSN: 2330-7668
2330-765X
Conference Name: Robotics: Systems and Science (RSS) (28 Jun 2009 - 01 Jul 2009 : Seattle, Washington, USA)
Statement of
Responsibility: 
Gabe Sibley, Christopher Mei, Ian Reid, Paul Newman
Abstract: It is well known that bundle adjustment is the optimal non-linear least-squares formulation of the simultaneous localization and mapping problem, in that its maximum likelihood form matches the definition of the Cramer Rao Lower Bound. Unfortunately, computing the ML solution is often prohibitively expensive – this is especially true during loop closures, which often necessitate adjusting all parameters in a loop. In this paper we note that it is precisely the choice of a single privileged coordinate frame that makes bundle adjustment costly, and that this expense can be avoided by adopting a completely relative approach. We derive a new relative bundle adjustment, which instead of optimizing in a single Euclidean space, works in a metric-space defined by a connected Riemannian manifold. Using an adaptive optimization strategy, we show experimentally that it is possible to solve for the full ML solution incrementally in constant time – even at loop closure. Our system also operates online in real-time using stereo data, with fast appearance-based loop closure detection. We show results for sequences of 23k frames over 1.08km that indicate the accuracy of the approach.
RMID: 0020131220
Published version: http://www.roboticsproceedings.org/rss05/p23.html
Appears in Collections:Computer Science publications

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