Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter

Foxlin, Eric

doi:10.1109/vrais.1996.490527

Cited by 308 publications

(217 citation statements)

References 11 publications

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“…A quaternion can be regarded as an element of 4 ℜ . In this paper, quaternions will be represented using the notation from [11]: where, providing n is a unit quaternion, (6) …”

Section: Quaternionsmentioning

confidence: 99%

“…The matrix R in Eqation (21) is defined by Equation (6). Because 0 y is measured and 1 y is known, the error between them is a function of the matrix M , which in turn depends on the four components of the quaternion.…”

Section: Algorithms For Quaternion Convergencementioning

confidence: 99%

“…This system utilized a fluid pendulum and three solid-state piezoelectric angular rate sensors. More recent publications describe the use of three orthogonal solid-state rate gyros, a twoaxis fluid inclinometer and a two-axis fluxgate compass [6]. Sensor data is processed by a complementary separate-bias Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

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

Marins

Yun²,

Bachmann³

et al.

Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics I

387

246

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This paper 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.

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“…A quaternion can be regarded as an element of 4 ℜ . In this paper, quaternions will be represented using the notation from [11]: where, providing n is a unit quaternion, (6) …”

Section: Quaternionsmentioning

confidence: 99%

Section: Algorithms For Quaternion Convergencementioning

confidence: 99%

See 1 more Smart Citation

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

Marins

Yun²,

Bachmann³

et al.

Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics I

387

246

View full text Add to dashboard Cite

show abstract

“…Foxlin [23] originally used a complementary separate-bias Kalman filter to combine gyroscopes, inclinometers, and a compass, while You and Neumann [24] use an extended Kalman filter with separate correction steps for vision and gyroscope updates. Jiang et al [25] combine vision and gyroscope sensors in a more heuristic manner-the gyroscope measurement is used as an initial estimate to limit the vision feature search, and the vision measurement is used to limit the gyroscope drift.…”

Section: High-quality Wide Area Trackingmentioning

confidence: 99%

“…Our complementary Kalman filter design is inspired by Foxlin's work on orientation tracker filtering [23]. The underlying concept is to filter the error signal between two sensors, rather than filtering the actual position estimate (see Fig.…”

Section: Complementary Kalman Filtermentioning

confidence: 99%

Heads Up and Camera Down: A Vision-Based Tracking Modality for Mobile Mixed Reality

DiVerdi

Höllerer

2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-Anywhere Augmentation pursues the goal of lowering the initial investment of time and money necessary to participate in mixed reality work, bridging the gap between researchers in the field, and regular computer users. Our paper contributes to this goal by introducing the GroundCam, a cheap tracking modality with no significant setup necessary. By itself, the GroundCam provides high frequency and high resolution relative position information similar to an inertial navigation system but with significantly less drift. We present the design and implementation of the GroundCam, analyze the impact of several design and runtime factors on tracking accuracy and consider the implications of extending our GroundCam to different hardware configurations. Motivated by the performance analysis, we developed a hybrid tracker that couples the GroundCam with a wide area tracking modality via a complementary Kalman filter, resulting in a powerful base for indoor and outdoor mobile mixed reality work. To conclude, the performance of the hybrid tracker and its utility within mixed reality applications is discussed.

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A Single Parameter Tunable Quaternion Based Attitude Estimation Filter

Bachmann

Yun

2006

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In human posture tracking applications, limb segment attitude can be estimated without the aid of a generated source using small inexpensive inertial/magnetic sensor modules. In the absence of an adequate process model and process noise characteristics or in an application in which the dynamic and measurement relations are non‐linear, a simple complementary filter represents a computationally inexpensive solution that produces accurate attitude estimates superior to those of an improperly tuned Kalman filter. This paper presents the theory and experimental results for a single parameter tunable quaternion based attitude estimation filter. The described sub‐optimal constant gain complementary filter is the first filter designed for inertial/magnetic sensor modules capable of estimating orientation in all attitudes without singularities while continuously correcting for drift without the need for still periods. Experimental results qualitatively and quantitatively demonstrate the accuracy and stability of the filtering algorithm.

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Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter

Cited by 308 publications

References 11 publications

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

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

Heads Up and Camera Down: A Vision-Based Tracking Modality for Mobile Mixed Reality

A Single Parameter Tunable Quaternion Based Attitude Estimation Filter

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