SLAMBench2Citation formats

  • External authors:
  • Bruno Bodin
  • Harry Wagstaff
  • Sajad Saecdi
  • Luigi Nardi
  • Emanuele Vespa
  • John Mawer
  • Andy Nisbet
  • Mikel Lujan
  • Andrew J. Davison
  • Paul H.J. Kelly
  • Michael F.P. O'Boyle

Standard

SLAMBench2 : Multi-Objective Head-to-Head Benchmarking for Visual SLAM. / Bodin, Bruno; Wagstaff, Harry; Saecdi, Sajad; Nardi, Luigi; Vespa, Emanuele; Mawer, John; Nisbet, Andy; Lujan, Mikel; Furber, Steve; Davison, Andrew J.; Kelly, Paul H.J.; O'Boyle, Michael F.P.

2018 IEEE International Conference on Robotics and Automation, ICRA 2018. IEEE, 2018. p. 3637-3644 8460558.

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Harvard

Bodin, B, Wagstaff, H, Saecdi, S, Nardi, L, Vespa, E, Mawer, J, Nisbet, A, Lujan, M, Furber, S, Davison, AJ, Kelly, PHJ & O'Boyle, MFP 2018, SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM. in 2018 IEEE International Conference on Robotics and Automation, ICRA 2018., 8460558, IEEE, pp. 3637-3644, 2018 IEEE International Conference on Robotics and Automation, Brisbane, Australia, 21/05/18. https://doi.org/10.1109/ICRA.2018.8460558

APA

Bodin, B., Wagstaff, H., Saecdi, S., Nardi, L., Vespa, E., Mawer, J., Nisbet, A., Lujan, M., Furber, S., Davison, A. J., Kelly, P. H. J., & O'Boyle, M. F. P. (2018). SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 (pp. 3637-3644). [8460558] IEEE. https://doi.org/10.1109/ICRA.2018.8460558

Vancouver

Bodin B, Wagstaff H, Saecdi S, Nardi L, Vespa E, Mawer J et al. SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018. IEEE. 2018. p. 3637-3644. 8460558 https://doi.org/10.1109/ICRA.2018.8460558

Author

Bodin, Bruno ; Wagstaff, Harry ; Saecdi, Sajad ; Nardi, Luigi ; Vespa, Emanuele ; Mawer, John ; Nisbet, Andy ; Lujan, Mikel ; Furber, Steve ; Davison, Andrew J. ; Kelly, Paul H.J. ; O'Boyle, Michael F.P. / SLAMBench2 : Multi-Objective Head-to-Head Benchmarking for Visual SLAM. 2018 IEEE International Conference on Robotics and Automation, ICRA 2018. IEEE, 2018. pp. 3637-3644

Bibtex

@inproceedings{fb5e2c189f7c4adda3956b210752cb92,
title = "SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM",
abstract = "SLAM is becoming 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 a problem since different SLAM applications can have different functional and non-functional requirements. For example, a mobile phone-based AR application has a tight energy budget, while a UAV navigation system usually requires high accuracy. SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM systems, both open and close source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. A wide variety of existing SLAM algorithms and datasets is supported, e.g. ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones is straightforward and clearly specified by the framework. SLAMBench2 is a publicly-available software framework which represents a starting point for quantitative, comparable and val-idatable experimental research to investigate trade-offs across SLAM systems.",
author = "Bruno Bodin and Harry Wagstaff and Sajad Saecdi and Luigi Nardi and Emanuele Vespa and John Mawer and Andy Nisbet and Mikel Lujan and Steve Furber and Davison, {Andrew J.} and Kelly, {Paul H.J.} and O'Boyle, {Michael F.P.}",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/ICRA.2018.8460558",
language = "English",
pages = "3637--3644",
booktitle = "2018 IEEE International Conference on Robotics and Automation, ICRA 2018",
publisher = "IEEE",
address = "United States",
note = "2018 IEEE International Conference on Robotics and Automation, ICRA 2018 ; Conference date: 21-05-2018 Through 25-05-2018",

}

RIS

TY - GEN

T1 - SLAMBench2

T2 - 2018 IEEE International Conference on Robotics and Automation

AU - Bodin, Bruno

AU - Wagstaff, Harry

AU - Saecdi, Sajad

AU - Nardi, Luigi

AU - Vespa, Emanuele

AU - Mawer, John

AU - Nisbet, Andy

AU - Lujan, Mikel

AU - Furber, Steve

AU - Davison, Andrew J.

AU - Kelly, Paul H.J.

AU - O'Boyle, Michael F.P.

PY - 2018/9/10

Y1 - 2018/9/10

N2 - SLAM is becoming 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 a problem since different SLAM applications can have different functional and non-functional requirements. For example, a mobile phone-based AR application has a tight energy budget, while a UAV navigation system usually requires high accuracy. SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM systems, both open and close source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. A wide variety of existing SLAM algorithms and datasets is supported, e.g. ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones is straightforward and clearly specified by the framework. SLAMBench2 is a publicly-available software framework which represents a starting point for quantitative, comparable and val-idatable experimental research to investigate trade-offs across SLAM systems.

AB - SLAM is becoming 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 a problem since different SLAM applications can have different functional and non-functional requirements. For example, a mobile phone-based AR application has a tight energy budget, while a UAV navigation system usually requires high accuracy. SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM systems, both open and close source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. A wide variety of existing SLAM algorithms and datasets is supported, e.g. ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones is straightforward and clearly specified by the framework. SLAMBench2 is a publicly-available software framework which represents a starting point for quantitative, comparable and val-idatable experimental research to investigate trade-offs across SLAM systems.

U2 - 10.1109/ICRA.2018.8460558

DO - 10.1109/ICRA.2018.8460558

M3 - Conference contribution

AN - SCOPUS:85055780631

SP - 3637

EP - 3644

BT - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018

PB - IEEE

Y2 - 21 May 2018 through 25 May 2018

ER -