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

Andrew Nisbet, John Mawer, Oscar Palomar Perez, Cosmin Gorgovan, Andrew Webb, James Clarkson, Graham Riley, Christos-Efthymios Kotselidis, Mikel Luján, Stephen Furber

Research output: Contribution to journalArticlepeer-review


Visual understanding of 3D environments in realtime, 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.
Original languageEnglish
JournalIEEE Proceedings
Issue number11
Early online date14 Aug 2018
Publication statusPublished - Nov 2018


  • SLAM
  • automatic performance tuning
  • hardware simulation
  • scheduling


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