An extended Kalman filter for quaternion-based orientation estimation using MARG sensors

By J. L. Marins, X. Yun, E. Bachmann, R. McGhee, M. Zyda. Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180), 2001

578 citations recorded by the source corpus.

Presents an extended Kalman filter for real-time estimation of rigid body orientation using the newly developed MARG (magnetic, angular rate, and gravity) sensors. Each MARG sensor contains a three-axis magnetometer, a three-axis angular rate sensor, and a three-axis accelerometer. The filter represents rotations using quaternions rather than Euler angles, which eliminates the long-standing problem of singularities associated with attitude estimation. A process model for rigid body angular motions and angular rate measurements is defined. The process model converts angular rates into quaternion rates, which are integrated to obtain quaternions. The Gauss-Newton iteration algorithm is utilized to find the best quaternion that relates the measured accelerations and earth magnetic field in the body coordinate frame to calculated values in the earth coordinate frame. The best quaternion is used as part of the measurements for the Kalman filter. As a result of this approach, the measurement equations of the Kalman filter become linear, and the computational requirements are significantly reduced, making it possible to estimate orientation in real time. Extensive testing of the filter with synthetic data and actual sensor data proved it to be satisfactory. Test cases included the presence of large initial errors as well as high noise levels. In all cases the filter was able to converge and accurately track rotational motions.

Subfields: Aerospace Engineering, Automotive Engineering

View Paper: https://www.semanticscholar.org/paper/c4d0c005a1ff3d2aab31a9d376c522c57aed7d4b

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Citations in the corpus

Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing (2011, methodology): “Clever algorithms are reported in the literature in order to reduce the computational burden of GN optimizers [25,42].”
Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs (2015, methodology): “[23] propose two different Kalman filter approaches to estimate the orientation in quaternion form from a MARG sensor.”
Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation (2005, methodology): “Also, other proposed methods to fuse the three different sensors do not take magnetic interferences into account [20]–[22].”
Estimation of IMU and MARG orientation using a gradient descent algorithm (2011, background): “The Kalman filter [9] has become the accepted basis for the majority of orientation algorithms [2], [10], [11], [12] and commercial inertial orientation sensors; xsens [13], microstrain [14], VectorNav [15], Intersense [16], PNI [17] and Crossbow [18] all produce systems founded on its use.”
Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing (2006, methodology): “In [7], the quaternion measurement is obtained by vector matching earth’s gravitational and magnetic ﬁelds, resolved by the aiding system in the body frame, with their known representation in the absolute reference frame [10].”
Complementary filter design on the special orthogonal group SO(3) (2005, methodology): “Traditional linear Kalman filter techniques [6] (including EKF techniques [2], [4]) have proved extremely difficult to apply robustly to applications with low quality sensor systems [5].”
An efficient orientation filter for inertial and inertial / magnetic sensor arrays (2010, background): “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).”
Fuzzy human motion analysis: A review (2014, methodology): “It ha s been successfully applied in several previous works on the human motion tracking [91, 108, 109, 110, 111, 112].”
Linear Quaternion Differential Equations: Basic Theory and Fundamental Results (2015, methodology): “In [32], the authors designed a Kalman filter for estimating orientation.”
Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds (2011, background): “The second alternative is to overparameterize states with a non-minimal representation such as unit quaternions or rotation matrices which are treated as R(4) or R3×3 respectively and re-normalized as needed [45, 30].”
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion (2017, methodology): “Works of Bachmann [30] and Marin [31] pioneered the field targeting orientation tracking of human limbs and robotic links by using mIMUs for computer graphics applications.”