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Learning inertial odometry for dynamic legged robot state estimation

Abstract:

This paper introduces a novel proprioceptive state estimator for legged robots based on a learned displacement measurement from IMU data. Recent research in pedestrian tracking has shown that motion can be inferred from inertial data using convolutional neural networks. A learned inertial displacement measurement can improve state estimation in challenging scenarios where leg odometry is unreliable, such as slipping and compressible terrains. Our work learns to estimate a displacement measure...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
Journal of Machine Learning Research
Series:
Proceedings of Machine Learning Research
Series number:
164
Pages:
1575-1584
Publication date:
2022-01-11
Acceptance date:
2021-09-01
Event title:
Conference: Conference on Robot Learning (CoRL 2021)
Event location:
London
Event website:
https://sites.google.com/robot-learning.org/corl2021
Event start date:
2021-11-08
Event end date:
2021-11-11
ISSN:
2640-3498
Language:
English
Keywords:
Pubs id:
1242877
Local pid:
pubs:1242877
Deposit date:
2022-03-09

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