An Improved Quaternion-Based Kalman Filter for Real-Time Tracking of Rigid Body Orientation
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Authors
Yun, Xiaoping
Lizarraga, Marian
Bachmann, Eric R.
McGhee, Robert B.
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Advisors
Date of Issue
2003-10
Date
Publisher
IEEE
Language
Abstract
This paper presents an improved Kalman filter for real-time tracking of human body motions. An earlier version of the filter was presented at IROS 2001. Since then, the filter has been substantially improved. Real-time tracking of rigid body orientation is accomplished using the MARG (magnetic, angular rate, and gravity) sensors. A MARG sensor measures the three-dimensional local magnetic field, three-dimensional angular rate, and three-dimensional acceleration. A Kalman filter is designed to process measurements provided by the MARG sensors, and to produce real-time orientation represented in quaternions. There are many design decisions as related to choice of state vectors, output equations, process model, etc. The filter design presented in this paper utilizes the Gauss-Newton method for parameter optimization in conjunction with Kalman filtering. The use of the Gauss-Newton method, particularly the reduced-order implementation introduced in the paper, significantly simplifies the Kalman filter design, and reduces computational requirements.
Type
Article
Description
The article of record as published may be found at https://doi.org/10.1109/IROS.2003.1248787.1109/TC.1977.5009272
Series/Report No
Department
Electrical and Computer Engineering (ECE)
Organization
Naval Postgraduate School (U.S.)
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Format
6 p.
Citation
Yun, Xiaoping, et al. "An improved quaternion-based Kalman filter for real-time tracking of rigid body orientation." Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453). Vol. 2. IEEE, 2003.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.