Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

Yujie Tang; Liang Hu; Qingrui Zhang; Wei Pan

doi:10.1109/IROS51168.2021.9635963

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/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

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 language	English
Title of host publication	2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6854-6859
Number of pages	6
ISBN (Electronic)	9781665417143
DOIs	https://doi.org/10.1109/IROS51168.2021.9635963
State	Published - 2021
Externally published	Yes
Event	2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 - Prague, Czech Republic Duration: 27 Sep 2021 → 1 Oct 2021

Publication series

Name	IEEE International Conference on Intelligent Robots and Systems
ISSN (Print)	2153-0858
ISSN (Electronic)	2153-0866

Conference

Conference	2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Country/Territory	Czech Republic
City	Prague
Period	27/09/21 → 1/10/21

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10.1109/IROS51168.2021.9635963

Cite this

Tang, Y., Hu, L., Zhang, Q., & Pan, W. (2021). Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 (pp. 6854-6859). (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS51168.2021.9635963

@inproceedings{dc5e58a392694df08a517fd0417372e2,

title = "Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation",

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.",

author = "Yujie Tang and Liang Hu and Qingrui Zhang and Wei Pan",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 ; Conference date: 27-09-2021 Through 01-10-2021",

year = "2021",

doi = "10.1109/IROS51168.2021.9635963",

language = "英语",

series = "IEEE International Conference on Intelligent Robots and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6854--6859",

booktitle = "2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021",

address = "美国",

}

Tang, Y, Hu, L, Zhang, Q & Pan, W 2021, Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation. in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021. IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 6854-6859, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, Prague, Czech Republic, 27/09/21. https://doi.org/10.1109/IROS51168.2021.9635963

Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation. / Tang, Yujie; Hu, Liang; Zhang, Qingrui et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 6854-6859 (IEEE International Conference on Intelligent Robots and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

AU - Tang, Yujie

AU - Hu, Liang

AU - Zhang, Qingrui

AU - Pan, Wei

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85124368470

U2 - 10.1109/IROS51168.2021.9635963

DO - 10.1109/IROS51168.2021.9635963

M3 - 会议稿件

AN - SCOPUS:85124368470

T3 - IEEE International Conference on Intelligent Robots and Systems

SP - 6854

EP - 6859

BT - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021

Y2 - 27 September 2021 through 1 October 2021

ER -

Tang Y, Hu L, Zhang Q, Pan W. Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 6854-6859. (IEEE International Conference on Intelligent Robots and Systems). doi: 10.1109/IROS51168.2021.9635963

Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

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