Details

Autor(en) / Beteiligte

• Saeedi, Sajad
• Spink, Tom
• Gorgovan, Cosmin
• Webb, Andrew
• Clarkson, James
• Tomusk, Erik
• Debrunner, Thomas
• Kaszyk, Kuba
• Gonzalez-De-Aledo, Pablo
• Rodchenko, Andrey
• Riley, Graham
• Bodin, Bruno
• Kotselidis, Christos
• Franke, Bjorn
• O'Boyle, Michael F.P.
• Davison, Andrew J.
• Kelly, Paul H. J.
• Lujan, Mikel
• Furber, Steve
• Wagstaff, Harry
• Nisbet, Andy
• Nardi, Luigi
• Mawer, John
• Melot, Nicolas
• Palomar, Oscar
• Vespa, Emanuele

Titel

Navigating the Landscape for Real-Time Localization and Mapping for Robotics and Virtual and Augmented Reality

Ist Teil von

• Proceedings of the IEEE, 2018-11, Vol.106 (11), p.2020-2039

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2018

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Visual understanding of 3-D environments in real time, at low power, is a huge computational challenge. Often referred to as simultaneous localization and mapping (SLAM), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, and 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 optimization 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.