%0 Generic
%A Tang, Yujie
%A Hu, Liang
%A Zhou, Zhipeng
%A Pan, Wei
%D 2024
%T code underlying publication: Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee
%U 
%R 10.4121/71bb6fd6-0983-442c-a266-fe3b7bee77e4.v1
%K Estimation error
%K Magnetometers
%K Inertial sensors
%K Quaternions
%K Reinforcement learning
%X <p><span style="background-color: rgb(255, 250, 234);">This collection contains all code to produce the results of </span><span style="color: rgb(51, 51, 51);">"Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee,"&nbsp;</span><em style="color: rgb(51, 51, 51);">2021 IEEE International Conference on Robotics and Automation (ICRA)</em><span style="color: rgb(51, 51, 51);">, Xi'an, China, 2021, pp. 10243-10249, doi: 10.1109/ICRA48506.2021.9561440. </span>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".</p>
%I 4TU.ResearchData