- 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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Grant:
- EP/M019918/1
- EP/J012017/1
- Publisher:
- IEEE Publisher's website
- Pages:
- 2835-2842
- Publication date:
- 2019-11-28
- Acceptance date:
- 2019-06-24
- DOI:
- Pubs id:
-
pubs:1033770
- URN:
-
uri:54c77405-0e9b-4899-a741-f7049a4724b6
- UUID:
-
uuid:54c77405-0e9b-4899-a741-f7049a4724b6
- Local pid:
- pubs:1033770
- ISBN:
- 978-1-5386-7024-8
- 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
Conference item
What could go wrong? Introspective radar odometry in challenging environments
Actions
Authors
Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Bibliographic Details
Item Description
Terms of use
Metrics
Altmetrics
Dimensions
If you are the owner of this record, you can report an update to it here: Report update to this record