@inproceedings{744625a7cf164baa8be5fd8fa84db16f,
title = "Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee",
abstract = "This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. Lyapunov's method in control theory is employed to prove the convergence of orientation estimation errors. The estimator gains and a Lyapunov function are parametrised by deep neural networks and learned from samples based on the theoretical results. The DRL estimator is compared with three well-known orientation estimation methods on both numerical simulations and real dataset collected from commercially available sensors. The results show that the proposed algorithm is superior for arbitrary estimation initialisation and can adapt to a drastic angular velocity profile for which other algorithms can be hardly applicable. To the best of our knowledge, this is the first DRL-based orientation estimation method with an estimation error boundedness guarantee.",
author = "Liang Hu and Yujie Tang and Zhipeng Zhou and Wei Pan",
note = "Funding Information: We thank Rick Staa (TU Delft) for implementing the Algorithm 1 in TensorFlow [32]. 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, Link”oping University, Sweden. We thank Gustaf Hendeby, Niklas Wahlstr”om, Hanna Nyqvist and Manon Kok who collected the real data and allow us to use. This work is supported by Huawei, AnKobot and China Scholarship Council (No.202006890020). Funding Information: We thank Rick Staa (TU Delft) for implementing the Algorithm 1 in TensorFlow [32]. 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, Link ”oping University, Sweden. We thank Gustaf Hendeby, Niklas Wahlstr ”om, Hanna Nyqvist and Manon Kok who collected the real data and allow us to use. This work is supported by Huawei, AnKobot and China Scholarship Council (No.202006890020). Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Conference on Robotics and Automation, ICRA 2021 ; Conference date: 30-05-2021 Through 05-06-2021",
year = "2021",
month = oct,
day = "18",
doi = "10.1109/ICRA48506.2021.9561440",
language = "English",
isbn = "9781728190778",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "IEEE",
pages = "10243--10249",
booktitle = "2021 IEEE International Conference on Robotics and Automation, ICRA 2021",
address = "United States",
}