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{Nardi:2015:10.1109/ICRA.2015.7140009,
author = {Nardi, L and Bodin, B and Zia, MZ and Mawer, J and Nisbet, A and Kelly, PHJ and Davison, AJ and Luján, M and O'Boyle, MFP and Riley, G and Topham, N and Furber, S},
doi = {10.1109/ICRA.2015.7140009},
pages = {5783--5790},
publisher = {IEEE},
title = {Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM},
url = {http://dx.doi.org/10.1109/ICRA.2015.7140009},
year = {2015}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging. Meanwhile, trends in low-cost, low-power processing are towards massive parallelism and heterogeneity, making it difficult for robotics and vision researchers to implement their algorithms in a performance-portable way. In this paper we introduce SLAMBench, 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 of a dense RGB-D SLAM system. SLAMBench provides a KinectFusion implementation in C++, OpenMP, OpenCL and CUDA, and harnesses the ICL-NUIM dataset of synthetic RGB-D sequences with trajectory and scene ground truth for reliable accuracy comparison of different implementation and algorithms. We present an analysis and breakdown of the constituent algorithmic elements of KinectFusion, and experimentally investigate their execution time on a variety of multicore and GPU-accelerated platforms. For a popular embedded platform, we also present an analysis of energy efficiency for different configuration alternatives.
AU  - Nardi,L
AU  - Bodin,B
AU  - Zia,MZ
AU  - Mawer,J
AU  - Nisbet,A
AU  - Kelly,PHJ
AU  - Davison,AJ
AU  - Luján,M
AU  - O'Boyle,MFP
AU  - Riley,G
AU  - Topham,N
AU  - Furber,S
DO  - 10.1109/ICRA.2015.7140009
EP  - 5790
PB  - IEEE
PY  - 2015///
SN  - 1050-4729
SP  - 5783
TI  - Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
UR  - http://dx.doi.org/10.1109/ICRA.2015.7140009
UR  - http://hdl.handle.net/10044/1/25780
ER  -
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