Low-effort place recognition with WiFi fingerprints using deep learning
This work addresses the need for low-effort localization systems for mobile devices and robots, though it is incremental as it builds on existing hierarchical approaches.
The paper tackled the problem of reducing the manual effort required for WiFi fingerprint-based indoor localization by using deep neural networks for building and floor classification, achieving satisfactory results on the UJIIndoorLoc dataset.
Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions.