Robust attitude estimation using an adaptive unscented Kalman filter

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dc.creatorAntônio Carlos Bana Chiella
dc.creatorBruno Otávio Teixeira
dc.creatorGuilherme Augusto Silva Pereira
dc.date.accessioned2025-05-09T12:04:38Z
dc.date.accessioned2025-09-08T23:50:55Z
dc.date.available2025-05-09T12:04:38Z
dc.date.issued2019
dc.identifier.doi10.1109/ICRA.2019.8793714
dc.identifier.urihttps://hdl.handle.net/1843/82170
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInternational Conference on Robotics and Automation (ICRA)
dc.rightsAcesso Restrito
dc.subjectModelos matemáticos
dc.subjectKalman, Filtragem de
dc.subject.otherQuaternions , Estimation , Covariance matrices , Magnetometers , Kalman filters , Accelerometers , Mathematical model
dc.subject.otherKalman Filter , Position Estimation , Adaptive Filter , Unscented Kalman Filter , Adaptive Kalman Filter , Adaptive Unscented Kalman Filter , Magnetic Field , Covariance Matrix , Linear Accelerator , Filter For Estimation , Robust Filter , Unit Quaternion , Kalman Filter For Estimation , Root Mean Square Error , Accelerometer , Measurement Uncertainty , Magnetometer , Measurement Noise , Euclidean Space , Inertial Measurement Unit , Rotation Vector , Bias Term , Exponential Map , Median Absolute Deviation , Covariance Estimation , Tangent Space , Inverse Mapping , Euler Angles , Angular Speed , Riemannian Manifold
dc.titleRobust attitude estimation using an adaptive unscented Kalman filter
dc.typeArtigo de evento
local.citation.epage7754
local.citation.spage7748
local.description.resumoThis paper presents the robust Adaptive unscented Kalman filter (RAUKF) for attitude estimation. Since the proposed algorithm represents attitude as a unit quaternion, all basic tools used, including the standard UKF, are adapted to the unit quaternion algebra. Additionally, the algorithm adopts an outlier detector algorithm to identify abrupt changes in the UKF innovation and an adaptive strategy based on covariance matching to tune the measurement covariance matrix online. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as magnetic field interference and linear accelerations. Experimental results with a manipulator robot suggest that our method overcomes other algorithms found in the literature.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://ieeexplore.ieee.org/document/8793714

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