Unsupervised online learning of condition-invariant images for place recognition
Lowry, Stephanie, Wyeth, Gordon, & Milford, Michael (2014) Unsupervised online learning of condition-invariant images for place recognition. In Proceedings of the Australasian Conference on Robotics and Automation 2014, Australian Robotics & Automation Association ARAA, University of Melbourne, Melbourne, Australia.
This paper presents an online, unsupervised training algorithm enabling vision-based place recognition across a wide range of changing environmental conditions such as those caused by weather, seasons, and day-night cycles. The technique applies principal component analysis to distinguish between aspects of a location’s appearance that are condition-dependent and those that are condition-invariant. Removing the dimensions associated with environmental conditions produces condition-invariant images that can be used by appearance-based place recognition methods. This approach has a unique benefit – it requires training images from only one type of environmental condition, unlike existing data-driven methods that require training images with labelled frame correspondences from two or more environmental conditions. The method is applied to two benchmark variable condition datasets. Performance is equivalent or superior to the current state of the art despite the lesser training requirements, and is demonstrated to generalise to previously unseen locations.
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|Item Type:||Conference Paper|
|Divisions:||Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 [Please consult the Authors]|
|Deposited On:||15 Dec 2014 04:44|
|Last Modified:||15 Dec 2014 22:50|
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