The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/71bb6fd6-0983-442c-a266-fe3b7bee77e4

Tang, Yujie; Hu, Liang; Zhou, Zhipeng; Pan, Wei (2024): code underlying publication: Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/71bb6fd6-0983-442c-a266-fe3b7bee77e4.v1

Dataset

• Automotive Engineering
• Engineering

Estimation error, Inertial sensors, Magnetometers, Quaternions, Reinforcement learning

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Yujie Tang , Liang Hu , Zhipeng Zhou , Wei Pan

This collection contains all code to produce the results of "Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee," 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 2021, pp. 10243-10249, doi: 10.1109/ICRA48506.2021.9561440. This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. To the best of our knowledge, this is the first DRL-based orientation estimation method using inertial sensors combined with a magnetometer. The code is written in Python. The packages used are listed in "requirements.txt". To reproduce the code, please refer to "README.md".

• 2024-10-29 first online, published, posted

4TU.ResearchData

.py

Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

TU Delft, Faculty of Mechanical Engineering, Department of Cognitive robotics