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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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Publisher copy:
10.1109/ICRA40945.2020.9196835

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-8940-7565
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Pembroke College
Role:
Author
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Name:
Engineering & Physical Sciences Research Council
Grant:
EP/M019918/1
Publisher:
IEEE
Journal:
Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA) More from this journal
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
Language:
English
Keywords:
Pubs id:
1112979
Local pid:
pubs:1112979
Deposit date:
2020-06-18

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