TY - GEN
T1 - Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation
AU - Tang, Yujie
AU - Hu, Liang
AU - Zhang, Qingrui
AU - Pan, Wei
N1 - Funding Information:
VI. ACKNOWLEDGEMENT We are grateful for the help and equipment provided by the UAS Technologies Lab, Artificial Intelligence and Integrated Computer Systems Division at the Department of Computer and Information Science, Linköping University, Sweden. We thank Gustaf Hendeby, Niklas Wahlström, Hanna Nyqvist and Manon Kok who collected the real data and allow us to use. WP is supported by HUAWEI and AnKobot. YT is supported by China Scholarship Council (No. 202006890020).
Publisher Copyright:
© 2021 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85124368470&partnerID=8YFLogxK
U2 - 10.1109/IROS51168.2021.9635963
DO - 10.1109/IROS51168.2021.9635963
M3 - Conference contribution
AN - SCOPUS:85124368470
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6854
EP - 6859
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PB - IEEE
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Y2 - 27 September 2021 through 1 October 2021
ER -