cff-version: 1.2.0
abstract: "<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>"
authors:
  - family-names: Tang
    given-names: Yujie
  - family-names: Hu
    given-names: Liang
  - family-names: Zhang
    given-names: Qingrui
  - family-names: Pan
    given-names: Wei
title: "Code underlying publication: Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/9da2c9da-9031-4b02-8c01-04f47494afd2.v1
license: MIT
date-released: 2024-10-29