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.
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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|Item Type:||Conference Paper|
|Keywords:||Whole-image descriptors, Robotics, Persistent place recognition, Temporal filtering, Sequential filtering techniques., Probabilistic mapping system|
|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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