Learning magnetic field distortion compensation for robotic systems

Abstract

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.