Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
- Submitting institution
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The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 40101631
- Type
- E - Conference contribution
- DOI
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10.1109/ICRA.2015.7140009
- Title of conference / published proceedings
- IEEE International Conference on Robotics and Automation (ICRA)
- First page
- 5783
- Volume
- -
- Issue
- -
- ISSN
- 1050-4729
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
11
- Research group(s)
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A - Computer Science
- Citation count
- 23
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Presents the first SLAM (Simultaneous Localisation and Mapping) framework to support evaluation of Computer Vision in terms of energy usage. Top 1% most cited papers of ICRA since 2014. Extended with two further (highly cited) ICRA publications developing the platform.
Used by the US Air Force (Institute of Technology) to benchmark algorithms for tracking algorithms fixed-wing aircraft: https://ieeexplore.ieee.org/document/9109945
Used by Movidius (an Intel subsidiary) to benchmark embedded vision algorithms: https://www.youtube.com/watch?v=OO-gupM8XJ0"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
