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In this paper we address the problem of indoor localization using magneticfield data in two setups, when data is collected by (i) human-held mobile phoneand (ii) by localization robots that perturb magnetic data with their ownelectromagnetic field. For the first setup, we revise the state of the artapproaches and propose a novel extended pipeline to benefit from the presenceof magnetic anomalies in indoor environment created by different ferromagneticobjects. We capture changes of the Earth's magnetic field due to indoormagnetic anomalies and transform them in multi-variate times series. We thenconvert temporal patterns into visual ones. We use methods of Recurrence Plots,Gramian Angular Fields and Markov Transition Fields to represent magnetic fieldtime series as image sequences. We regress the continuous values of userposition in a deep neural network that combines convolutional and recurrentlayers. For the second setup, we analyze how magnetic field data get perturbedby robots' electromagnetic field. We add an alignment step to the mainpipeline, in order to compensate the mismatch between train and test setsobtained by different robots. We test our methods on two public (MagPie andIPIN'20) and one proprietary (Hyundai department store) datasets. We reportevaluation results and show that our methods outperform the state of the artmethods by a large margin.

Magnetic Field Sensing for Pedestrian and Robot Indoor Positioning