Magnetic field SLAM exploration: Frequency domain Gaussian processes and informative route planning

Abstract

In this paper, we consider magnetic field SLAM exploration using a mobile robot with a magnetometer and wheel encoders. We propose computationally feasible solutions to model magnetic fields using frequency domain Gaussian processes. In addition, we propose a path planning algorithm to efficiently collect a given level of accuracy for magnetic field models. The path planning is based on partition of shortest paths into blocks with similar information content and implementing depth-first search among these blocks. Finally, we propose an exploration-exploitation algorithm enabling real-world mobile robot SLAM exploration solutions with motion uncertainties. SLAM is presented with Rao-Blackwellized particle filters where robot's path hypothesis are presented with particles together with a separate magnetic field model for each particle. We conducted SLAM exploration experiments using real magnetic field data in a simulated environment. Simulation parameters were tuned to approximate ICreate robot's motion uncertainties and MicroMag3 magnetometer's sensor noise, together with the robot's inclination uncertainties. Simulations demonstrated for the first time that we were able to build actual magnetic field SLAM exploration. The results indicate that, with frequency domain Gaussian processes, we are able to obtain desirable convergence of path distribution, although, with the selected particle filter SLAM approach, the localization accuracy was not desirable.