Learning Inertial Odometry for Dynamic Legged Robot State Estimation

Russell Buchanan, Marco Camurri, Frank Dellaert, Maurice Fallon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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 measurement from IMU data which is then fused with traditional leg odometry. Our approach greatly reduces the drift of proprioceptive state estimation, which is critical for legged robots deployed in vision and lidar denied environments such as foggy sewers or dusty mines. We compared results from an EKF and an incremental fixed-lag factor graph estimator using data from several real robot experiments crossing challenging terrains. Our results show a reduction of relative pose error by 37% in challenging scenarios when compared to a traditional kinematic-inertial estimator without learned measurement. We also demonstrate a 22% reduction in error when used with vision systems in visually degraded environments such as an underground mine.
Original languageEnglish
Title of host publicationProceedings of the 5th Conference on Robot Learning
EditorsAleksandra Faust, David Hsu, Gerhard Neumann
PublisherPMLR
Pages1575-1584
Number of pages10
Volume164
Publication statusPublished - 1 Aug 2022
EventThe Conference on Robot Learning 2021 - London, United Kingdom
Duration: 8 Nov 202111 Nov 2021
Conference number: 5
https://sites.google.com/robot-learning.org/corl2021/home

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
ISSN (Electronic)2640-3498

Conference

ConferenceThe Conference on Robot Learning 2021
Abbreviated titleCoRL 2021
Country/TerritoryUnited Kingdom
CityLondon
Period8/11/2111/11/21
Internet address

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