# Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

arXiv: https://arxiv.org/abs/2103.02357 · HTML: https://mlsanity.com/paper/2103.02357/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem](https://mlsanity.com/paper/1701.08376/) (2017)
- [Actor-Critic Reinforcement Learning for Control with Stability Guarantee](https://mlsanity.com/paper/2004.14288/) (2020)
- [Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation](https://mlsanity.com/paper/2002.10718/) (2020)
- [Using Inertial Sensors for Position and Orientation Estimation](https://mlsanity.com/paper/1704.06053/) (2017)
- [Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles](https://mlsanity.com/paper/2008.07240/) (2020)
- [Lyapunov-Based Reinforcement Learning State Estimator](https://mlsanity.com/paper/2010.13529/) (2020)
- [Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties](https://mlsanity.com/paper/2003.13839/) (2020)

## Built on by (most cited first, 1 of 1)

- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)

## Abstract (arXiv)

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.