Data-driven Covariance Tuning of the Extended Kalman Filter for Visual-based Pose Estimation of the Stewart Platform

This paper explores the quaternion representation in order to devise an extended Kalman filter approach for pose estimation: inertial measurements are fused with visual data so as to estimate the position and orientation of a six degrees-of-freedom rigid body. The filter equations are described alon...

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Bibliographic Details
Published in:Journal of control, automation & electrical systems automation & electrical systems, 2023-08, Vol.34 (4), p.720-730
Main Authors: Salton, Aurélio T., Pimentel, Guilherme A., Melo, José V., Castro, Rafael S., Benfica, Juliano
Format: Article
Language:English
Subjects:
Algorithms
Control
Control and Systems Theory
Covariance matrix
Degrees of freedom
Electrical Engineering
Engineering
Extended Kalman filter
Mechatronics
Pose estimation
Quaternions
Rigid structures
Robotics
Robotics and Automation
Tuning
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Summary:This paper explores the quaternion representation in order to devise an extended Kalman filter approach for pose estimation: inertial measurements are fused with visual data so as to estimate the position and orientation of a six degrees-of-freedom rigid body. The filter equations are described along with a data-driven tuning method that selects the model covariance matrix based on experimental results. Finally, the proposed algorithm is applied to a six degrees-of-freedom Stewart platform, a representative system of a large class of industrial manipulators that could benefit from the proposed solution.
ISSN:2195-3880
2195-3899
DOI:10.1007/s40313-023-01006-4