Journal article
Selective sensor fusion for neural visual-inertial odometry
- Abstract:
-
Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data. We propose a novel end-to-end selective sensor fusion framework for monocular VIO, which fuses monocular images and inertial measurements in order to estimate the trajectory whilst improving robustness to real-life issues, such as missing and corrupted data or bad sensor synchronization. In particular...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Journal:
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Journal website
- Pages:
- 10534-10543
- Publication date:
- 2019-06-01
- Acceptance date:
- 2019-03-11
- Event title:
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Event location:
- Long Beach, California, USA
- Event start date:
- 2019-06-15
- Event end date:
- 2019-06-20
- DOI:
- ISBN:
- 9781728132938
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1079494
- Local pid:
- pubs:1079494
- Deposit date:
- 2020-02-20
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2019
- Rights statement:
- © 2019 IEEE
- Notes:
- This is the accepted manuscript version of the paper. The final version is available online from IEEE at: https://doi.org/10.1109/CVPR.2019.01079
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