Magnetic Field-based Indoor Localization of a Tracked Robot with Simultaneous Calibration

Abstract

Magnetic-field based indoor localization uses distortions in the Earth magnetic field caused by magnetic material in the surrounding of the platform that is to be localized. Existing magnetic localization approaches compare the measurements of a magnetometer to a map of the magnetic field to estimate the position of the platform the sensor is mounted on. For the comparison it is typically assumed that the magnetometer is calibrated. Unfortunately, for some platforms calibration is not straightforward, particularly when they have a large extend or high weight. In this paper we therefore propose a simultaneous localization and calibration approach. The approach is based on a Rao-Blackwellized particle filter that makes use of the conditional linearity of magnetometer measurements and calibration parameters. The feasibility of the proposed particle filter is evaluated with three data sets recorded with a differential drive robot in an indoor environment.