Magnetic field sensing for pedestrian and robot indoor positioning

In this paper we address the problem of indoor localization using magnetic field data in two setups, when data is collected by (i) a 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 an extended pipeline to benefit from the presence of magnetic anomalies in indoor environments created by different ferromagnetic objects. We capture changes of the Earth’s magnetic field due to indoor magnetic anomalies and transform them into multi-variate times series. We then propose to 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 continuous user position by combining convolutional and recurrent layers in deep neural networks. For the second setup, we analyse how magnetic field data get perturbed by robots’ electromagnetic field. We propose an alignment step to compensate the mismatch between train and test sets obtained by different robots. We test our methods on two public datasets (MagPie [11] and IPIN’20 [4]) and one proprietary (Hyundai department store). We report evaluation results and show that our methods outperform the state of the art methods by a large margin.