A parameterized geometric magnetic field calibration method for vehicles with moving masses with applications to underwater gliders
A parameterized geometric magnetic field calibration method for vehicles with moving masses with applications to underwater gliders
Date
2016-04
Authors
Claus, Brian
Bachmayer, Ralf
Bachmayer, Ralf
Linked Authors
Alternative Title
Citable URI
As Published
Date Created
Location
DOI
Related Materials
Replaces
Replaced By
Keywords
Abstract
The accuracy of magnetic measurements performed by autonomous vehicles is often limited
by the presence of moving ferrous masses. This work presents a parameterized ellipsoid
eld calibration method for magnetic measurements in the sensor frame. In this manner
the ellipsoidal calibration coe cients are dependent on the locations of the moving masses.
The parameterized calibration method is evaluated through eld trials with an autonomous
underwater glider equipped with a low power precision
uxgate sensor. A rst set of eld
trials were performed in the East Arm of Bonne Bay, Newfoundland in December of 2013.
During these trials a series of calibration pro les with the mass shifting and ballast mecha-
nisms at di erent locations were performed before and after the survey portion of the trials.
Further trials were performed in the Labrador Sea in July of 2014 with two reduced sets
of calibration runs. The nominal ellipsoidal coe cients were extracted using the full set
of measurements from a set of calibration pro les and used as the initial conditions for
the polynomials which de ne each parameterized coe cient. These polynomials as well as
the sensor misalignment matrix were then optimized using a gradient descent solver which
minimizes both the total magnetic eld di erence and the vertical magnetic eld variance
between the modeled and measured values. Including the vertical eld in this manner allows
for convergence in spite of severe limitations on the platform's motion and for computation
of the vehicle's magnetic heading.
Description
Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 34 (2017): 209-223, doi:10.1002/rob.21660.