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Kidnapped radar: topological radar localisation using rotationally-invariant metric learning

Abstract:

This paper presents a system for robust, large-scale topological localisation using Frequency-Modulated ContinuousWave (FMCW) scanning radar. We learn a metric space for embedding polar radar scans using CNN and NetVLAD architectures traditionally applied to the visual domain. However, we tailor the feature extraction for more suitability to the polar nature of radar scan formation using cylindrical convolutions, anti-aliasing blurring, and azimuth-wise max-pooling; all in order to bolster th...

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Publication status:
Published
Peer review status:
Reviewed (other)

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Publisher:
IEEE Xplore Publisher's website
Journal:
Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA) Journal website
Issue:
2020
Pages:
4358-4364
Publication date:
2020-09-15
Acceptance date:
2020-01-21
Event title:
IEEE International Conference on Robotics and Automation (ICRA)
Event location:
Palais des Congrès de Paris
Event website:
https://www.icra2020.org/
Event start date:
2020-05-31T00:00:00Z
Event end date:
2020-06-04T00:00:00Z
DOI:
EISSN:
2577-087X
ISSN:
1050-4729
Pubs id:
1088404
Local pid:
pubs:1088404
Language:
English
Keywords:

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