Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

Yujie Tang; Liang Hu; Qingrui Zhang; Wei Pan

IROS 2021poster24 citations

Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

Yujie Tang, Liang Hu, Qingrui Zhang, Wei Pan

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.

BibTeX

@inproceedings{iros2021_reinforcementlea,
  title = {Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation},
  author = {Yujie Tang and Liang Hu and Qingrui Zhang and Wei Pan},
  booktitle = {IROS 2021},
  year = {2021}
}