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dc.contributor.authorSaeedi, Sajad
dc.contributor.authorBodin, Bruno
dc.contributor.authorWagstaff, Harry
dc.contributor.authorNisbet, Andy
dc.contributor.authorNardi, Luigi
dc.contributor.authorMawer, John
dc.contributor.authorMelot, Nicolas
dc.contributor.authorPalomar, Oscar
dc.contributor.authorVespa, Emanuele
dc.contributor.authorSpink, Tom
dc.contributor.authorGorgovan, Cosmin
dc.contributor.authorWebb, Andrew
dc.contributor.authorClarkson, James
dc.contributor.authorTomusk, Erik-Arne
dc.contributor.authorDebrunner, Thomas
dc.contributor.authorKaszyk, Kuba
dc.contributor.authorGonzalez-de-Aledo, Pablo
dc.contributor.authorRodchenko, Andrey
dc.contributor.authorRiley, Graham
dc.contributor.authorKotselidis, Christos
dc.contributor.authorFranke, Bjoern
dc.contributor.authorO'Boyle, Michael
dc.contributor.authorDavison, Andrew J
dc.contributor.authorKelly, Paul H. J.
dc.contributor.authorLuján, Mikel
dc.contributor.authorFurber, Steve
dc.identifier.citationSaeedi , S , Bodin , B , Wagstaff , H , Nisbet , A , Nardi , L , Mawer , J , Melot , N , Palomar , O , Vespa , E , Spink , T , Gorgovan , C , Webb , A , Clarkson , J , Tomusk , E-A , Debrunner , T , Kaszyk , K , Gonzalez-de-Aledo , P , Rodchenko , A , Riley , G , Kotselidis , C , Franke , B , O'Boyle , M , Davison , A J , Kelly , P H J , Luján , M & Furber , S 2018 , ' Navigating the landscape for real-time localization and mapping for robotics and virtual and augmented reality ' , Proceedings of the IEEE , vol. 106 , no. 11 , pp. 2020 - 2039 .
dc.identifier.otherPURE: 276650357
dc.identifier.otherPURE UUID: 0e69d2c8-d567-4893-96fc-e5a377b7cf17
dc.identifier.otherRIS: urn:12D548CAC62C57382850A171D8276CE4
dc.identifier.otherScopus: 85051650765
dc.identifier.otherORCID: /0000-0002-7662-3146/work/103138172
dc.descriptionThis work was supported by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K008730/1, PAMELA Projecten
dc.description.abstractVisual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches, and (4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.
dc.relation.ispartofProceedings of the IEEEen
dc.rightsCopyright © 2018 the Author(s). This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see
dc.subjectAutomatic Performance Tuningen
dc.subjectHardware Simulationen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQA76 Computer softwareen
dc.subjectT Technologyen
dc.titleNavigating the landscape for real-time localization and mapping for robotics and virtual and augmented realityen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.description.statusPeer revieweden

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