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Addressing challenging place recognition tasks using generative adversarial networks

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dc.contributor.authorLatif, Y.
dc.contributor.authorGarg, R.
dc.contributor.authorMilford, M.
dc.contributor.authorReid, I.
dc.contributor.conferenceIEEE International Conference on Robotics and Automation (ICRA) (21 May 2018 - 25 May 2018 : Brisbane, Australia)
dc.date.issued2018
dc.description.abstractPlace recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception task since the same place is perceptually very different in the morning, at night, or over different seasons. This work addresses place recognition as a domain translation task. Using a pair of coupled Generative Adversarial Networks (GANs), we show that it is possible to generate the appearance of one domain (such as summer) from another (such as winter) without requiring image-to-image correspondences across the domains. Mapping between domains is learned from sets of images in each domain without knowing the instanceto- instance correspondence by enforcing a cyclic consistency constraint. In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition. Experiments show that learned features correspond to visual similarity and can be effectively used for place recognition across seasons.
dc.description.statementofresponsibilityYasir Latif, Ravi Garg, Michael Milford and Ian Reid
dc.identifier.citationIEEE International Conference on Robotics and Automation, 2018, pp.2349-2355
dc.identifier.doi10.1109/ICRA.2018.8461081
dc.identifier.isbn1538630818
dc.identifier.isbn9781538630815
dc.identifier.issn1050-4729
dc.identifier.issn2577-087X
dc.identifier.orcidLatif, Y. [0000-0002-2529-5322]
dc.identifier.orcidGarg, R. [0000-0002-9422-8086]
dc.identifier.orcidReid, I. [0000-0001-7790-6423]
dc.identifier.urihttp://hdl.handle.net/2440/124483
dc.language.isoen
dc.publisherIEEE
dc.publisher.placePiscataway, NJ.
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100016
dc.relation.granthttp://purl.org/au-research/grants/arc/FL130100102
dc.relation.ispartofseriesIEEE International Conference on Robotics and Automation ICRA
dc.rights©2018 IEEE
dc.source.urihttps://ieeexplore.ieee.org/xpl/conhome/8449910/proceeding
dc.titleAddressing challenging place recognition tasks using generative adversarial networks
dc.typeConference paper
pubs.publication-statusPublished

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