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IONet: Learning to Cure the Curse of Drift in Inertial Odometry

Abstract:

Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error when accelerations are double integrated to obtain displacement. Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds. We propose to break the cycle of continuous integration...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Oxford college:
Kellogg College
Role:
Author
ORCID:
0000-0001-5716-3941
Publisher:
Association for the Advancement of Artificial Intelligence
Host title:
Thirty-Second AAAI Conference on Artificial Intelligence
Journal:
Thirty-Second AAAI Conference on Artificial Intelligence More from this journal
Pages:
6468-6476
Publication date:
2018-02-02
Acceptance date:
2017-11-08
ISBN:
9781577358008
Keywords:
Pubs id:
pubs:832757
UUID:
uuid:cb60db07-a31f-4971-a44a-b23cd311847d
Local pid:
pubs:832757
Source identifiers:
832757
Deposit date:
2019-10-16

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