Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

Yujie Tang, Liang Hu, Qingrui Zhang, Wei Pan*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Inertial measurement units are widely used in different fields to estimate the attitude. Many algorithms have been proposed to improve estimation performance. However, most of them still suffer from 1) inaccurate initial estimation, 2) inaccurate initial filter gain, and 3) non-Gaussian process and/or measurement noise. This paper will leverage reinforcement learning to compensate for the classical extended Kalman filter estimation, i.e., to learn the filter gain from the sensor measurements. We also analyse the convergence of the estimate error. The effectiveness of the proposed algorithm is validated on both simulated data and real data.
Original languageEnglish
Title of host publicationProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
PublisherIEEE
Pages6854-6859
ISBN (Electronic)978-1-6654-1714-3
ISBN (Print)978-1-6654-1715-0
DOIs
Publication statusPublished - 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Online at Prague, Czech Republic
Duration: 27 Sep 20211 Oct 2021

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
CountryCzech Republic
CityOnline at Prague
Period27/09/211/10/21

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