Cart (Loading....) | Create Account
Close category search window
 

IMU Self-Calibration Using Factorization

Sign In

Full text access may be available.

To access full text, please use your member or institutional sign in.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Myung Hwangbo ; Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Jun-Sik Kim ; Kanade, T.

This paper presents a convenient self-calibration method for an inertial measurement unit (IMU) using matrix factorization. Using limited information about applied loads (accelerations or angular rates) available from natural references, the proposed method can linearly solve all the parameters of an IMU in any configuration of its inertial components. Our factorization-based calibration method exploits the bilinear form of an IMU measurement, which is the product of intrinsic calibration parameters and exerted loads. For a redundant IMU, we prove that partial knowledge of the loads, such as magnitude, can produce a linear solution space for a proper decomposition of the measurement. Theoretical analysis on this linear space reveals that a 1-D null space should be considered when load magnitudes are all equal (e.g., gravity loads). Degenerate load distributions are also geometrically identified to avoid singular measurement collection. Since a triad IMU has a lower number of sensor components than a 4-D parameter space, we propose an iterative factorization in which only initial bias is required. A wide convergence region of the bias can provide an automatic setting of the initial bias as the mean of the measurements. Performance of the proposed method is evaluated with respect to various noise levels and constraint types. Self-calibration capability is demonstrated using natural references, which are gravity for accelerometers and image stream from an attached camera for gyroscopes. Calibration results are globally optimal and identical to those of nonlinear optimization.

Published in:

Robotics, IEEE Transactions on  (Volume:29 ,  Issue: 2 )

Date of Publication:

April 2013

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.