Computer vision; Extended Kalman filter (EKF); Inertial measurement units (IMU); Quartenion; Sensor fusion; State estimation; Data driven; Extended kalman filter; Filter approach; Inertial measurement unit; Inertial measurements units; Pose-estimation; Quaternion representation; Stewart platforms; Control and Systems Engineering; Energy Engineering and Power Technology; Computer Science Applications; Electrical and Electronic Engineering
Abstract :
[en] 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.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Salton, Aurélio T. ; School of Engineering, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
Araujo pimentel, Guilherme ; Université de Mons - UMONS > Faculté Polytechnique > Service Systèmes, Estimation, Commande et Optimisation
Melo, José V.; Group of Automation and Control of Systems (GACS), School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
Castro, Rafael S.; Group of Automation and Control of Systems (GACS), School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
Benfica, Juliano; Group of Automation and Control of Systems (GACS), School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
Language :
English
Title :
Data-driven Covariance Tuning of the Extended Kalman Filter for Visual-based Pose Estimation of the Stewart Platform
Publication date :
August 2023
Journal title :
Journal of Control, Automation and Electrical Systems
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