Training-free probability models for whole-image based place recognition
Contribuinte(s) |
Katupitiya, Jayantha Guivant, Jose Eaton, Ray |
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Data(s) |
2013
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Resumo |
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. |
Identificador | |
Publicador |
Australian Robotics & Automation Association |
Relação |
http://www.araa.asn.au/acra/acra2013/papers/pap124s1-file1.pdf 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. http://purl.org/au-research/grants/ARC/DP110103006 |
Direitos |
Copyright 2013 [please consult the authors] |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #Whole-image descriptors #Robotics #Persistent place recognition #Temporal filtering #Sequential filtering techniques. #Probabilistic mapping system |
Tipo |
Conference Paper |