Navigating the landscape for real-time localization and mapping for robotics and virtual and augmented reality
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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.
Saeedi , 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 . https://doi.org/10.1109/JPROC.2018.2856739
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Copyright © 2018 the Author(s). This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
DescriptionThis work was supported by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K008730/1, PAMELA Project
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