Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing

By Angelo Maria Sabatini. IEEE Transactions on Biomedical Engineering, 2006

922 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/003662e7d455ee0547c1c28b98de387b81c03ae6

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Citations in the corpus

Gait Analysis Using Wearable Sensors (2012, methodology): “To incorporate the excellent dynamics of a gyroscope and the stable, drift-free performance of an accelerometer, a Kalman-based fusion algorithm was applied to obtain dynamic orientations of body segments [117].”
Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs (2015, methodology): “The quaternion-based EKF presented by Sabatini [16] is similar to the first approach of Marins et al. [23], but the state is composed by the unit quaternion augmented with accelerometer and magnetometer bias vectors for on-line calibration.”
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion (2017, methodology): “, [10,31,38,75,92]) so that it is possible to at least qualitatively assess drift by visual inspection of the RMSE along time.”
RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods (2019, background): “A standard sensor fusion algorithm works well for the device orientation estimation [15], [16].”
An efficient orientation filter for inertial and inertial / magnetic sensor arrays (2010, methodology): “The filter employs a quaternion representation of orientation (as in: [34, 17, 24, 30, 32]) to describe the coupled nature of orientations in three-dimensions and is not subject to the problematic singularities associated with an Euler angle representation(1).”
Using Inertial Sensors for Position and Orientation Estimation (2017, neutral): “For orientation estimation, they are often used in combination with magnetometers, which measure the direction of the magnetic field [124, 120].”
Complementary Observer for Body Segments Motion Capturing by Inertial and Magnetic Sensors (2014, positive): “This triad of sensors is used also in [15], [16] to develop an Extended Kalman Filter (EKF).”
Activity identification using body-mounted sensors—a review of classification techniques (2009, background): “Inertial sensors can be complemented by a magnetic compass or magnetometer (Parkka et al 2006), which can enable more accurate orientation measurement about the vertical axis (Sabatini 2006) and by GPS (Murakami and Makikawa 1997) to enable location tracking.”
Globally exponentially stable cascade observers for attitude estimation (2012, methodology): “The Extended Kalman Filter (EKF) has been at the core of numerous stochastic solution, see e.g. Farrell (1970), Bar-Itzhack and Oshman (1985), and Sabatini (2006), while nonlinear alternatives, aiming for stability and convergence properties, have been proposed in Sanyal, Lee, Leok, and McClamroch…”
Estimation of IMU and MARG orientation using a gradient descent algorithm (2011, methodology): “The algorithm employs a quaternion representation of orientation (as in: [25], [12], [19]) to describe the coupled nature of orientations in three-dimensions and is not subject to the problematic singularities associated with an Euler angle representation.”
Zero-Velocity Detection—An Algorithm Evaluation (2010, background): “Zero-velocity updates are well suited for bounding the error growth of a foot-mounted INS, as during ordinary gait the foot returns to a stationary state on a regular base [19].”
Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing (2011, methodology): “In other terms, the horizontal reference is built and maintained by the EKF itself (auto-calibration) [21,22].”
Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity (2013, methodology): “The use of gyroscopes in addition to acceleration sensors could be regarded as the solution for separating the gravitational component from the acceleration signal [14,15,16].”