Training-free probability models for whole-image based place recognition

Lowry, Stephanie, Wyeth, Gordon, & Milford, Michael (2013) Training-free probability models for whole-image based 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. 1-9.

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Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

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ID Code: 66626
Item Type: Conference Paper
Refereed: Yes
Keywords: Whole-image descriptors, Robotics, Persistent place recognition, Temporal filtering, Sequential filtering techniques., Probabilistic mapping system
ISBN: 9780980740448
ISSN: 1448-2053
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 01:41
Last Modified: 09 Apr 2014 12:20

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