%0 Generic
%A Tang, Yujie
%A Hu, Liang
%A Zhang, Qingrui
%A Pan, Wei
%D 2024
%T Code underlying publication: Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation
%U 
%R 10.4121/9da2c9da-9031-4b02-8c01-04f47494afd2.v1
%K Measurement units
%K Estimation
%K Reinforcement learning
%K Gain measurement
%K Filtering algorithms
%K Robot sensing systems
%K Kalman filters
%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 Compensated Extended Kalman Filter for Attitude Estimation,"&nbsp;</span><em style="color: rgb(51, 51, 51);">2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</em><span style="color: rgb(51, 51, 51);">, Prague, Czech Republic, 2021, pp. 6854-6859, doi: 10.1109/IROS51168.2021.9635963. </span>This paper leverages reinforcement learning to compensate for the classical extended Kalman filter estimation, i.e., to learn the filter gain from the sensor</p><p>measurements. <span style="color: rgb(51, 51, 51);">The code is written in python. To use the code, the readers could set up the Python environment according to "requirements.txt." For details, please follow "README.md". </span></p>
%I 4TU.ResearchData