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We present a method for scalable and fully 3D magnetic field simultaneouslocalisation and mapping (SLAM) using local anomalies in the magnetic field asa source of position information. These anomalies are due to the presence offerromagnetic material in the structure of buildings and in objects such asfurniture. We represent the magnetic field map using a Gaussian process modeland take well-known physical properties of the magnetic field into account. Webuild local maps using three-dimensional hexagonal block tiling. To make ourapproach computationally tractable we use reduced-rank Gaussian processregression in combination with a Rao-Blackwellised particle filter. We showthat it is possible to obtain accurate position and orientation estimates usingmeasurements from a smartphone, and that our approach provides a scalablemagnetic field SLAM algorithm in terms of both computational complexity and mapstorage.

Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps