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

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...

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Bibliographic Details
Published in:arXiv.org 2018-08
Main Authors: Saeedi, Sajad, Bodin, Bruno, Wagstaff, Harry, Nisbet, Andy, Nardi, Luigi, Mawer, John, Melot, Nicolas, Palomar, Oscar, Vespa, Emanuele, Spink, Tom, Gorgovan, Cosmin, Webb, Andrew, Clarkson, James, Tomusk, Erik, Debrunner, Thomas, Kuba Kaszyk, Gonzalez-de-Aledo, Pablo, Rodchenko, Andrey, Riley, Graham, Kotselidis, Christos, Franke, Björn, O'Boyle, Michael F P, Davison, Andrew J, Kelly, Paul H J, Luján, Mikel, Furber, Steve
Format: Article
Language:English
Subjects:
Algorithms
Augmented reality
Automation
Computer simulation
Energy consumption
Industrial robots
Localization
Machine learning
Manufacturing engineering
Optimization
Quantitative analysis
Real time
Robotics
Simultaneous localization and mapping
Software
Virtual reality
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1808.06352