MAIL

Abstract

Knowing accurate indoor locations of pedestrians has great social and commercial values, such as pedestrian heatmapping and targeted advertising. Location estimation with sequential inputs (e.g., geomagnetic sequences) has received much attention lately, mainly because they enhance the localization accuracy with temporal correlations. Nevertheless, it is challenging to realize accurate localization with geomagnetic sequences due to environmental factors, such as non-uniform ferromagnetic disturbances. To address this, we propose MAIL, a multi-scale attention-guided indoor localization network, which turns these challenges into favorable advantages. Our key contributions are as follows. First, instead of extracting a single holistic feature from an input sequence directly, we design a scale-based feature extraction unit that takes variational anomalies at different scales into consideration. Second, we propose an attention generation scheme that identifies attention values for different scales. Rather than setting fixed numbers, MAIL learns them adaptively with the input sequence, thus increasing its adaptability and generality. Third, guided by attention values, we fuse multi-scale features by paying more attention to prominent ones and estimate current location with the fused feature. We evaluate the performance of MAIL in three different trial sites. Evaluation results show that MAIL reduces the mean localization error by more than 36% compared with the state-of-the-art competing schemes.