Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/115715
Type: Conference paper
Title: Efficient geometric matching with polar bounds for aligning star field images
Author: Marker, R.
Chin, T.
Newsam, G.
Citation: Proceedings of the Australasian Conference on Robotics and Automation 2016, 2017 / Singh, S., Kurniawati, H., Pounds, P. (ed./s), vol.2016-December, pp.185-193
Publisher: ARAA
Issue Date: 2017
ISBN: 978-1-63439-608-0
ISSN: 1448-2053
Conference Name: Australasian Conference on Robotics and Automation (ACRA-2016) (05 Dec 2016 - 07 Dec 2016 : Brisbane, QLD)
Statement of
Responsibility: 
Ryan James Marker, Tat-Jun Chin, Garry Newsam
Abstract: For many autonomous satellites, maintaining current knowledge of their location and pose is critical to mission success. Whilst there exist mature solutions for positioning and attitude estimation in space, computer vision approaches are emerging as viable and cheaper alternatives. At the core of a vision-based satellite motion estimation system is registering the successive images taken in space. This is a challenging task due to the lack of visually salient features in star eld images. In this research, we propose a novel technique for registering star eld images. We formulate the task as a geometric matching optimisation problem, which is solved using branch-and-bound. Our main contribution lies in devising a novel polar bounding function that provides tighter bounds than existing methods. Our bounding function is also easy to evaluate, requiring no more computations than available bounding functions. As demonstrated on actual space images, both factors contribute to speed up geometric matching by a large degree. The proposed technique further illustrates the promise of vision-based sensors for space missions.
Rights: Copyright status unknown
RMID: 0030085873
Published version: http://www.araa.asn.au/category/acra-2016/
Appears in Collections:Computer Science publications

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