- 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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Reviewed (other)
- Files:
-
-
(, 0.0b)
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- Publisher copy:
- 10.1109/ICRA40945.2020.9196682
-
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- 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:
- Copyright holder:
- IEEE
- Copyright date:
- 2020
- Rights statement:
- © 2020 IEEE
- Notes:
- This is an accepted manuscript version of the article. The final version is available from IEEE Xplore at: https://doi.org/10.1109/ICRA40945.2020.9196682
Conference item
Kidnapped radar: topological radar localisation using rotationally-invariant metric learning
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