Learning Magnetic Field Distortion Compensation for Robotic Systems
Leif Christensen, Mario Michael Krell, Frank Kirchner
In Intelligent Robots and Systems (IROS), (IROS), 24.9.-28.9.2017, Vancouver, BC, IEEE, Sep/2017.
The work presented in this paper describes the
use and evaluation of machine learning techniques like neural
networks and support vector regression to learn a model of
magnetic field distortions often induced in inertial measurement
units using magnetometers by changing currents, postures or
configurations of a robotic system. Such a model is needed in
order to compensate the local dynamic distortions, especially
for complex and confined robotic systems, and to achieve more
robust and accurate ambient magnetic field measurements.
This is crucial for a wide variety of autonomous navigation
purposes from simple heading estimation over standard SLAM
approaches to sophisticated magnetic field based localization
techniques. The approach was evaluated in a laboratory setup
and with a complex robotic system in an outdoor environment.