Past, present, and future of simultaneous localization and mapping: toward the robust-perception age

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dc.contributor.authorCadena, C.
dc.contributor.authorCarlone, L.
dc.contributor.authorCarrillo, H.
dc.contributor.authorLatif, Y.
dc.contributor.authorScaramuzza, D.
dc.contributor.authorNeira, J.
dc.contributor.authorReid, I.
dc.contributor.authorLeonard, J.
dc.date.issued2016
dc.description.abstractSimultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?
dc.description.statementofresponsibilityCesar Cadena, Luca Carlone, Henry Carrillo
dc.identifier.citationIEEE Transactions on Robotics, 2016; 32(6):1309-1332
dc.identifier.doi10.1109/TRO.2016.2624754
dc.identifier.issn1552-3098
dc.identifier.issn1941-0468
dc.identifier.orcidLatif, Y. [0000-0002-2529-5322]
dc.identifier.orcidReid, I. [0000-0001-7790-6423]
dc.identifier.urihttp://hdl.handle.net/2440/107554
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.grantARC
dc.relation.grantDP130104413
dc.relation.grantCE140100016
dc.relation.grantFL130100102
dc.relation.grant609763
dc.relation.grantEU-H2020-688652
dc.relation.grantSERI-15.0284
dc.rightsCopyright © 2016, IEEE
dc.source.urihttps://doi.org/10.1109/tro.2016.2624754
dc.subjectGraph theory; simultaneous location and mapping; service robots; robustness; localization
dc.titlePast, present, and future of simultaneous localization and mapping: toward the robust-perception age
dc.typeJournal article
pubs.publication-statusPublished

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