Large Scale Mapping of Indoor Magnetic Field by Local and Sparse Gaussian Processes

Abstract

Magnetometer-based indoor navigation uses variations in the magnetic field to determine the robot’s location. For that, a magnetic map of the environment has to be built beforehand from a collection of localized magnetic measurements. Existing solutions built on sparse Gaussian Process (GP) regression do not scale well to large environments, being either slow or resulting in discontinuous prediction. In this paper, we propose to model the magnetic field of large environments based on GP regression. We first modify a deterministic training conditional sparse GP by accounting for magnetic field physics to map small environments efficiently. We then scale the model on larger scenes by introducing a local expert aggregation framework. It splits the scene into subdomains, fits a local expert on each, and then aggregates expert predictions in a differentiable and probabilistic way. We evaluate our model on real and simulated data and show that we can smoothly map a three-story building in a few hundred milliseconds.