Abstract
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.
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Notes
In Algorithm 1, \({\varvec{x}}_k^-\) and \({\varvec{P}}_k^-\) denote a priori estimates, i.e., prior to the application of the correction step.
The normalization of a quaternion in \({\varvec{x}}\) is computed as \({\varvec{q}} \leftarrow {\varvec{q}} / \Vert {\varvec{q}} \Vert \), where \(\Vert \cdot \Vert \) stands for the Euclidian norm and \({\varvec{q}}\) represents the quaternion component of \({\varvec{x}}\) as in (19).
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A. T. Salton acknowledges the support from CNPq Brazil under Grant 306214/2018-0.
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Salton, A.T., Pimentel, G.A., Melo, J.V. et al. Data-driven Covariance Tuning of the Extended Kalman Filter for Visual-based Pose Estimation of the Stewart Platform. J Control Autom Electr Syst 34, 720–730 (2023). https://doi.org/10.1007/s40313-023-01006-4
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DOI: https://doi.org/10.1007/s40313-023-01006-4