  This paper presents a deep reinforcement learning (DRL) algorithm for
orientation estimation using inertial sensors combined with magnetometer. The
Lyapunov method in control theory is employed to prove the convergence of
orientation estimation errors. Based on the theoretical results, the estimator
gains and a Lyapunov function are parametrized by deep neural networks and
learned from samples. The DRL estimator is compared with three well-known
orientation estimation methods on both numerical simulations and real datasets
collected from commercially available sensors. The results show that the
proposed algorithm is superior for arbitrary estimation initialization and can
adapt to very large angular velocities for which other algorithms can be hardly
applicable. To the best of our knowledge, this is the first DRL-based
orientation estimation method with estimation error boundedness guarantee.
