Imperial College London
Alternate

ProfessorAndrewDavison

Faculty of Engineering, Department of Computing

Professor of Robot Vision
 
 
 
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Contact

 

+44 (0)20 7594 8316a.davison Website

 
 
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Assistant

 

Ms Lucy Atthis +44 (0)20 7594 8259

 
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Location

 

303William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Saeedi:2018:10.1109/JPROC.2018.2856739,
author = {Saeedi, Gharahbolagh S and Bodin, B and Wagstaff, H and Nisbet, A and Nardi, L and Mawer, J and Melot, N and Palomar, O and Vespa, E and Gorgovan, C and Webb, A and Clarkson, J and Tomusk, E and Debrunner, T and Kaszyk, K and Gonzalez, P and Rodchenko, A and Riley, G and Kotselidis, C and Franke, B and OBoyle, M and Davison, A and Kelly, P and Lujan, M and Furber, S},
doi = {10.1109/JPROC.2018.2856739},
journal = {Proceedings of the IEEE},
pages = {2020--2039},
title = {Navigating the landscape for real-time localisation and mapping for robotics, virtual and augmented reality},
url = {http://dx.doi.org/10.1109/JPROC.2018.2856739},
volume = {106},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Saeedi,Gharahbolagh S
AU  - Bodin,B
AU  - Wagstaff,H
AU  - Nisbet,A
AU  - Nardi,L
AU  - Mawer,J
AU  - Melot,N
AU  - Palomar,O
AU  - Vespa,E
AU  - Gorgovan,C
AU  - Webb,A
AU  - Clarkson,J
AU  - Tomusk,E
AU  - Debrunner,T
AU  - Kaszyk,K
AU  - Gonzalez,P
AU  - Rodchenko,A
AU  - Riley,G
AU  - Kotselidis,C
AU  - Franke,B
AU  - OBoyle,M
AU  - Davison,A
AU  - Kelly,P
AU  - Lujan,M
AU  - Furber,S
DO  - 10.1109/JPROC.2018.2856739
EP  - 2039
PY  - 2018///
SN  - 0018-9219
SP  - 2020
TI  - Navigating the landscape for real-time localisation and mapping for robotics, virtual and augmented reality
T2  - Proceedings of the IEEE
UR  - http://dx.doi.org/10.1109/JPROC.2018.2856739
UR  - https://ieeexplore.ieee.org/document/8436423
UR  - http://hdl.handle.net/10044/1/61930
VL  - 106
ER  -
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