A Novel Quaternion Kalman Filter

Choukroun, Daniel; Bar-Itzhack, Itzhack Y.; Oshman, Yaakov

doi:10.2514/6.2002-4460

Cited by 77 publications

(162 citation statements)

References 9 publications

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“…To mention just a few references for the particular application of filtering on SO(3), a filter for random walks on the tangent bundle (with the only system noise being additive noise in the Lie algebra corresponding to velocities) was developed in Chiuso and Soatto [2000], a quaternion representation was used with projection and a Kalman filter adapted to the curved space in Choukroun et al [2006], and Lee et al [2008] proposes a method to propagate uncertainty under continuous time dynamics in a noise-free setting. The particle filter approach in Kwon et al [2007] has already been mentioned.…”

Section: Background and Related Workmentioning

confidence: 99%

Robust point-mass filters on manifolds

Tidefelt

Schön

2009

IFAC Proceedings Volumes

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Section: Background and Related Workmentioning

confidence: 99%

Robust point-mass filters on manifolds

Tidefelt

Schön

2009

IFAC Proceedings Volumes

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“…However, in the implementation of the Kalman filter, the unit norm constraint must be enforced to the estimated quaternion using, e.g., simple vector normalization (Choukroun et al 2006). …”

Section: Extended Kalman Filtermentioning

confidence: 99%

Interaction Control of Robot Manipulators Using Force and Vision

Lippiello

Siciliano

Villani

2008

UOPT

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An approach to force and visual control of robot manipulators in contact with a partially known environment is proposed in this article. The environment is modeled as a rigid object of known geometry but of unknown and time-varying position and orientation. An algorithm for online estimation of the object pose is adopted, based on visual data provided by a camera as well as on forces measured during the interaction. This information is used by a force/position control scheme, in charge of managing the interaction. Simulation and experimental results are presented for the case of an industrial robot manipulator.

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“…It provides optimal error correction for noisy and inaccurately modelled random processes through a recursive algorithm which accumulates information regarding the process characteristics. Recently, the Kalman algorithm has been formulated in the quaternion domain representation to track in three-dimensional spaces [1], [2], [3].…”

Section: Introductionmentioning

confidence: 99%

A regularised EEG informed Kalman filtering algorithm

Enshaeifar

Spyrou

Sanei

et al. 2016

Biomedical Signal Processing and Control

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The conventional Kalman filter assumes a constant process noise covariance according to the system's dynamics. However, in practice, the dynamics might alter and the initial model for the process noise may not be adequate to adapt to abrupt dynamics of the system. In this paper, we provide a novel informed Kalman filter (IKF) which is informed by an extrinsic data channel carrying information about the system's future state. Thus, each state can be represented with a corresponding process noise covariance, i.e. the Kalman gain is automatically adjusted according to the detected state. As a real-world application, we demonstrate for the first time how the analysis of electroencephalogram (EEG) can be used to predict the voluntary body movement and inform the tracking Kalman algorithm about a possible state transition. Furthermore, we provide a rigorous analysis to establish a relationship between the Kalman performance and the detection accuracy. Simulations on both synthetic and real-world data support our analysis.

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A Novel Quaternion Kalman Filter

Cited by 77 publications

References 9 publications

Robust point-mass filters on manifolds

Robust point-mass filters on manifolds

Interaction Control of Robot Manipulators Using Force and Vision

A regularised EEG informed Kalman filtering algorithm

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