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Navigating the Landscape for Real-time Localisation and Mapping for Robotics, Virtual and Augmented Reality

Research output: Contribution to journalArticle

  • Sajad Saeedi
  • Andy Nisbet
  • Luigi Nardi
  • John Mawer
  • Nicolas Melot
  • Oscar Palomar
  • Emanuele Vespa
  • Cosmin Gorgovan
  • Andrew Webb
  • James Clarkson
  • Thomas Debrunner
  • Pablo Gonzalez-de-Aledo
  • Andrey Rodchenko
  • Graham Riley
  • Christos Kotselidis
  • Andrew J Davison
  • Paul H. J. Kelly
  • Mikel Luján
  • Steve Furber

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Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalProceedings of the IEEE
Issue number99
Publication statusPublished - 14 Aug 2018


Visual 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.

    Research areas

  • SLAM, Automatic Performance Tuning, Hardware Simulation, Scheduling

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