Magnetic field-based SLAM method for solving the localization problem in mobile robot floor-cleaning task

In this paper we present a SLAM method based on indoor magnetic field anomalies and measure the acquired map quality in the context of the localization problem present in mobile robot floor-cleaning scenarios. According to our real-world robot experiments in different environments, it appears that most modern buildings have sufficient magnetic field variation to make the method applicable in mobile robot floor-cleaning tasks. We show that our method can be used to acquire maps that are accurate enough to be utilized in the robot coverage problem, thus reducing over-cleaning. We use Gaussian Processes to model the magnetic field and a Rao-Blackwellized Particle Filter to estimate the pose distribution of the robot. Because magnetic field anomalies are not correlated to typical features used in localization, our method can handle many situations in which other methods fail. The minimalistic sensory requirements of our method make it a very viable alternative for low-cost domestic robots.