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This paper presents a deep reinforcement learning (DRL) algorithm fororientation estimation using inertial sensors combined with magnetometer. TheLyapunov method in control theory is employed to prove the convergence oforientation estimation errors. Based on the theoretical results, the estimatorgains and a Lyapunov function are parametrized by deep neural networks andlearned from samples. The DRL estimator is compared with three well-knownorientation estimation methods on both numerical simulations and real datasetscollected from commercially available sensors. The results show that theproposed algorithm is superior for arbitrary estimation initialization and canadapt to very large angular velocities for which other algorithms can be hardlyapplicable. To the best of our knowledge, this is the first DRL-basedorientation estimation method with estimation error boundedness guarantee.

Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee