Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

Liang Hu, Yujie Tang, Zhipeng Zhou, Wei Pan

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages10243-10249
ISBN (Electronic)978-1-7281-9077-8
ISBN (Print)978-1-7281-9078-5
DOIs
Publication statusPublished - 2021
EventICRA 2021: IEEE International Conference on Robotics and Automation - Hybrid at Xi'an, China
Duration: 30 May 20215 Jun 2021

Conference

ConferenceICRA 2021
CountryChina
CityHybrid at Xi'an
Period30/05/215/06/21

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