Accelerating FAB-MAP With Concentration Inequalities

We outline an approach for using concentration inequalities to perform rapid approximate multi-hypothesis testing. In a scenario where multiple hypotheses are ranked according to a large set of features, our scheme improves the efficiency of selecting the best hypothesis by providing a bail-out thre...

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Main Authors: Cummins, M, Newman, P
Format: Journal article
Language:English
Published: 2010
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author Cummins, M
Newman, P
author_facet Cummins, M
Newman, P
author_sort Cummins, M
collection OXFORD
description We outline an approach for using concentration inequalities to perform rapid approximate multi-hypothesis testing. In a scenario where multiple hypotheses are ranked according to a large set of features, our scheme improves the efficiency of selecting the best hypothesis by providing a bail-out threshold at which unpromising hypotheses can be excluded from further evaluation. We show how concentration inequalities can be used to derive principled bail-out thresholds, subject to a user-specified error tolerance. The technique is similar to the sequential probability ratio test, but is applicable in more general conditions. We apply the technique to improve the speed of the fast-appearance-based mapping system for appearance-based place recognition and mapping. The speed increase provided by the new approach is data dependent, but we demonstrate speed improvements of between 25x 50x on real data, with only a slight degradation in accuracy. © 2010 IEEE.
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spelling oxford-uuid:c0c1945b-8c84-4a75-b8ce-a6576b9cff332022-03-27T05:56:41ZAccelerating FAB-MAP With Concentration InequalitiesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c0c1945b-8c84-4a75-b8ce-a6576b9cff33EnglishSymplectic Elements at Oxford2010Cummins, MNewman, PWe outline an approach for using concentration inequalities to perform rapid approximate multi-hypothesis testing. In a scenario where multiple hypotheses are ranked according to a large set of features, our scheme improves the efficiency of selecting the best hypothesis by providing a bail-out threshold at which unpromising hypotheses can be excluded from further evaluation. We show how concentration inequalities can be used to derive principled bail-out thresholds, subject to a user-specified error tolerance. The technique is similar to the sequential probability ratio test, but is applicable in more general conditions. We apply the technique to improve the speed of the fast-appearance-based mapping system for appearance-based place recognition and mapping. The speed increase provided by the new approach is data dependent, but we demonstrate speed improvements of between 25x 50x on real data, with only a slight degradation in accuracy. © 2010 IEEE.
spellingShingle Cummins, M
Newman, P
Accelerating FAB-MAP With Concentration Inequalities
title Accelerating FAB-MAP With Concentration Inequalities
title_full Accelerating FAB-MAP With Concentration Inequalities
title_fullStr Accelerating FAB-MAP With Concentration Inequalities
title_full_unstemmed Accelerating FAB-MAP With Concentration Inequalities
title_short Accelerating FAB-MAP With Concentration Inequalities
title_sort accelerating fab map with concentration inequalities
work_keys_str_mv AT cumminsm acceleratingfabmapwithconcentrationinequalities
AT newmanp acceleratingfabmapwithconcentrationinequalities