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What could go wrong? Introspective radar odometry in challenging environments

Abstract:

This paper is about detecting failures under uncertainty and improving the reliability of radar-only motion estimation. We use weak supervision together with inertial measurement fusion to train a classifier that exploits the principal eigenvector associated with our radar scan matching algorithm at run-time and produces a prior belief in the robot’s motion estimate. This prior is used in a filtering framework to correct for vehicle motion estimates. We demonstrate the system on a challenging...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ITSC.2019.8917111

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Pembroke College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Grant:
CAV2 – Stream 1 CRD (DRIVEN
Publisher:
IEEE Publisher's website
Journal:
IEEE Intelligent Transport Systems Conference Journal website
Pages:
2835-2842
Host title:
2019 IEEE Intelligent Transportation Systems Conference (ITSC)
Publication date:
2019-11-28
Acceptance date:
2019-06-24
Event location:
Auckland, New Zealand
Event start date:
2019-10-27T00:00:00Z
Event end date:
2019-10-30T00:00:00Z
DOI:
Source identifiers:
1033770
ISBN:
9781538670248
Keywords:
Pubs id:
pubs:1033770
UUID:
uuid:54c77405-0e9b-4899-a741-f7049a4724b6
Local pid:
pubs:1033770
Deposit date:
2019-07-22

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