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: 10.4121/9da2c9da-9031-4b02-8c01-04f47494afd2

Tang, Yujie; Hu, Liang; Zhang, Qingrui; Pan, Wei (2024): Code underlying publication: Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/9da2c9da-9031-4b02-8c01-04f47494afd2.v1

Dataset

• Automotive Engineering
• Engineering

Estimation, Filtering algorithms, Gain measurement, Kalman filters, Measurement units, Reinforcement learning, Robot sensing systems

MIT

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

by Yujie Tang , Liang Hu , Qingrui Zhang , Wei Pan

This collection contains all code to produce the results of "Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021, pp. 6854-6859, doi: 10.1109/IROS51168.2021.9635963. This paper leverages reinforcement learning to compensate for the classical extended Kalman filter estimation, i.e., to learn the filter gain from the sensor

measurements. 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".

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

4TU.ResearchData

written in python (.py)

Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

• China Scholarship Council (CSC) under Grant 202006890020 (grant code 202006890020) China Scholarship Council

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