Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

Conference paper (2021)

Authors

Y. Tang Robot Dynamics - Mechanical, Maritime and Materials Engineering
Liang Hu University of Essex
Qingrui Zhang Sun Yat-sen University
W. Pan Robot Dynamics - Mechanical, Maritime and Materials Engineering
Research Group
Robot Dynamics (Mechanical, Maritime and Materials Engineering) (TU Delft)
More Info
expand_more

Published Date

2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Robot Dynamics

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.

Files

Reinforcement_Learning_Compens... (pdf)
(pdf | 1.79 Mb)

Download not available