A Parameterized Geometric Magnetic Field Calibration Method for Vehicles with Moving Masses with Applications to Underwater Gliders
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 field calibration method for magnetic measurements in the sensor frame. In this manner, the ellipsoidal calibration coefficie...
| Published in: | Journal of Field Robotics |
|---|---|
| Main Authors: | , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2016
|
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1002/rob.21660 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Frob.21660 https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.21660 |
| Summary: | 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 field calibration method for magnetic measurements in the sensor frame. In this manner, the ellipsoidal calibration coefficients are dependent on the locations of the moving masses. The parameterized calibration method is evaluated through field trials with an autonomous underwater glider equipped with a low power precision fluxgate sensor. A first set of field trials were performed in the East Arm of Bonne Bay, Newfoundland, in December 2013. During these trials, a series of calibration profiles with the mass shifting and ballast mechanisms at different locations were performed before and after the survey portion of the trials. Further trials were performed in the Labrador Sea in July 2014 with two reduced sets of calibration runs. The nominal ellipsoidal coefficients were extracted using the full set of measurements from a set of calibration profiles and used as the initial conditions for the polynomials, which define each parameterized coefficient. These polynomials as well as the sensor misalignment matrix were then optimized using a gradient descent solver, which minimizes both the total magnetic field difference and the vertical magnetic field variance between the modeled and measured values. Including the vertical field 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. |
|---|