Conference item
Under the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radar
- Abstract:
-
This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar. By embedding a differentiable point-based motion estimator inside our architecture, we learn keypoint locations, scores and descriptors from localisation error alone. This approach avoids imposing any assumption on what makes a robust keypoint and crucially allows them to be optimised for our application. Furthermore the architecture is sensor agnos...
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- Publication status:
- Published
- Peer review status:
- Reviewed (other)
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Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Journal:
- Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA) Journal website
- Pages:
- 9484-9490
- Publication date:
- 2020-09-15
- Acceptance date:
- 2019-12-20
- Event title:
- International Conference on Robotics and Automation 2020
- Event location:
- Online
- Event website:
- https://www.icra2020.org/
- Event start date:
- 2020-05-31
- Event end date:
- 2020-08-31
- DOI:
- EISSN:
-
2577-087X
- ISSN:
-
1050-4729
- EISBN:
- 978-1-7281-7395-5
- ISBN:
- 978-1-7281-7396-2
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1112979
- Local pid:
- pubs:1112979
- Deposit date:
- 2020-06-18
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2020
- Rights statement:
- © 2020 IEEE
- Notes:
- This paper was presented at the 2020 IEEE International Conference on Robotics and Automation (ICRA), 31st May - 31st August 2020. This is the accepted manuscript version of the paper. The publisher's version is available from IEEE at: https://doi.org/10.1109/ICRA40945.2020.9196835
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