Conference item
What could go wrong? Introspective radar odometry in challenging environments
- Abstract:
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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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- Files:
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(Accepted manuscript, pdf, 2.6MB)
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- Publisher copy:
- 10.1109/ITSC.2019.8917111
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Authors
Funding
Bibliographic Details
- 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
Item Description
- Keywords:
- Pubs id:
-
pubs:1033770
- UUID:
-
uuid:54c77405-0e9b-4899-a741-f7049a4724b6
- Local pid:
- pubs:1033770
- Deposit date:
- 2019-07-22
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2019
- Notes:
- Copyright © 2019 IEEE. This is the accepted manuscript version of the paper. The final version is available online from IEEE at: https://doi.org/10.1109/ITSC.2019.8917111
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