  In this paper we address the problem of indoor localization using magnetic
field data in two setups, when data is collected by (i) human-held mobile phone
and (ii) by localization robots that perturb magnetic data with their own
electromagnetic field. For the first setup, we revise the state of the art
approaches and propose a novel extended pipeline to benefit from the presence
of magnetic anomalies in indoor environment created by different ferromagnetic
objects. We capture changes of the Earth's magnetic field due to indoor
magnetic anomalies and transform them in multi-variate times series. We then
convert temporal patterns into visual ones. We use methods of Recurrence Plots,
Gramian Angular Fields and Markov Transition Fields to represent magnetic field
time series as image sequences. We regress the continuous values of user
position in a deep neural network that combines convolutional and recurrent
layers. For the second setup, we analyze how magnetic field data get perturbed
by robots' electromagnetic field. We add an alignment step to the main
pipeline, in order to compensate the mismatch between train and test sets
obtained by different robots. We test our methods on two public (MagPie and
IPIN'20) and one proprietary (Hyundai department store) datasets. We report
evaluation results and show that our methods outperform the state of the art
methods by a large margin.
