Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps
This provides a scalable SLAM solution for indoor navigation using magnetic fields, but it is incremental as it builds on existing methods like Gaussian processes and particle filters.
The paper tackles the problem of scalable 3D magnetic field SLAM by using local anomalies as position information, achieving accurate position and orientation estimates with smartphone measurements.
We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping (SLAM) using local anomalies in the magnetic field as a source of position information. These anomalies are due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture. We represent the magnetic field map using a Gaussian process model and take well-known physical properties of the magnetic field into account. We build local maps using three-dimensional hexagonal block tiling. To make our approach computationally tractable we use reduced-rank Gaussian process regression in combination with a Rao-Blackwellised particle filter. We show that it is possible to obtain accurate position and orientation estimates using measurements from a smartphone, and that our approach provides a scalable magnetic field SLAM algorithm in terms of both computational complexity and map storage.