Towards condition-invariant, top-down visual place recognition
Milford, Michael, Vig, Eleonora, Scheirer, Walter, & Cox, David (2013) Towards condition-invariant, top-down visual place recognition. In Katupitiya, Jayantha, Guivant, Jose, & Eaton, Ray (Eds.) Proceedings of the 2013 Australasian Conference on Robotics & Automation, Australian Robotics & Automation Association, University of New South Wales, Sydney, NSW, pp. 1-10.
In this paper we present a novel place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images (although we use a cohort normalization score to exploit temporal frame information), alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We demonstrate the algorithm on the challenging Alderley sunny day – rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. The system is able to achieve 21.24% recall at 100% precision, matching drastically different day and night-time images of places while successfully rejecting match hypotheses between highly aliased images of different places. The results provide a new benchmark for single image, condition-invariant place recognition.
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|Item Type:||Conference Paper|
|Keywords:||Robotic vision, Place recognition algorithm|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 [please consult the authors]|
|Deposited On:||28 Jan 2014 03:07|
|Last Modified:||09 Apr 2014 12:20|
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