Imperial College London
Alternate

ProfessorPaulKelly

Faculty of Engineering, Department of Computing

Professor of Software Technology
 
 
 
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Contact

 

+44 (0)20 7594 8332p.kelly Website

 
 
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Location

 

Level 3 (upstairs), William Penney Building, room 304William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Bodin:2018:10.1109/ISPASS.2018.00024,
author = {Bodin, B and Nardi, L and Wagstaff, H and Kelly, PHJ and O'Boyle, M},
doi = {10.1109/ISPASS.2018.00024},
pages = {123--124},
title = {Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications},
url = {http://dx.doi.org/10.1109/ISPASS.2018.00024},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Simultaneous Localisation And Mapping (SLAM) is a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify the interface of such algorithms, or to perform a holistic comparison of their capabilities. This is particularly true when it comes to evaluate the potential trade-offs between computation speed, accuracy, and power consumption. SLAMBench is a benchmarking framework to evaluate existing and future SLAM systems, both open and closed source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. SLAMBench is a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption across SLAM systems. In this poster we give an overview of SLAMBench and in particular we show how this framework can be used within Design Space Exploration and large-scale performance evaluation on mobile phones.
AU  - Bodin,B
AU  - Nardi,L
AU  - Wagstaff,H
AU  - Kelly,PHJ
AU  - O'Boyle,M
DO  - 10.1109/ISPASS.2018.00024
EP  - 124
PY  - 2018///
SP  - 123
TI  - Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications
UR  - http://dx.doi.org/10.1109/ISPASS.2018.00024
ER  -
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