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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/139965
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dc.contributor.authorSchubert, S.-
dc.contributor.authorNeubert, P.-
dc.contributor.authorGarg, S.-
dc.contributor.authorMilford, M.-
dc.contributor.authorFischer, T.-
dc.date.issued2023-
dc.identifier.citationIEEE Robotics and Automation magazine, 2023; 2-16-
dc.identifier.issn1070-9932-
dc.identifier.issn1558-223X-
dc.identifier.urihttps://hdl.handle.net/2440/139965-
dc.descriptionOnlinePubl-
dc.description.abstractLocalization is an essential capability for mobile robots, enabling them to build a comprehensive representation of their environment and interact with the environment effectively toward a goal. A rapidly growing field of research in this area is visual place recognition (VPR), which is the ability to recognize previously seen places in the world based solely on images.-
dc.description.statementofresponsibilityStefan Schubert, Peer Neubert, Sourav Garg, Michael Milford, and Tobias Fischer-
dc.language.isoen-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)-
dc.rights© 2023 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. 2 For more information, see https://creativecommons.org/licenses/by/4.0/-
dc.source.urihttp://dx.doi.org/10.1109/mra.2023.3310859-
dc.subjectSimultaneous localization and mapping; Tutorials; Visualization; Pipelines; Global navigation satellite system; Robots; History-
dc.titleVisual Place Recognition: A Tutorial-
dc.typeJournal article-
dc.identifier.doi10.1109/mra.2023.3310859-
dc.relation.granthttp://purl.org/au-research/grants/arc/FL210100156-
pubs.publication-statusPublished-
dc.identifier.orcidGarg, S. [0000-0001-6068-3307]-
Appears in Collections:Australian Institute for Machine Learning publications

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