Visual RSSI Fingerprinting for Radio-based Indoor Localization

Giuseppe Puglisi, Daniele Di Mauro, Antonino Furnari, Luigi Gulino, Giovanni Farinella

2022

Abstract

The problem of localizing objects exploiting RSSI signals has been tackled using both geometric and machine learning based methods. Solutions machine learning based have the advantage to better cope with noise, but require many radio signal observations associated to the correct position in the target space. This data collection and labeling process is not trivial and it typically requires building a grid of dense observations, which can be resource-intensive. To overcome this issue, we propose a pipeline which uses an autonomous robot to collect RSSI-image pairs and Structure from Motion to associate 2D positions to the RSSI values based on the inferred position of each image. This method, as we shown in the paper, allows to acquire large quantities of data in an inexpensive way. Using the collected data, we experiment with machine learning models based on RNNs and propose an optimized model composed of a set of LSTMs that specialize on the RSSI observations coming from different antennas. The proposed method shows promising results outperforming different baselines, suggesting that the proposed pipeline allowing to collect and automatically label observations is useful in real scenarios. Furthermore, to aid research in this area, we publicly release the collected dataset comprising 57158 RSSI observations paired with RGB images.

Paper Citation

in Harvard Style

Puglisi G., Di Mauro D., Furnari A., Gulino L. and Farinella G. (2022). Visual RSSI Fingerprinting for Radio-based Indoor Localization. In Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, ISBN 978-989-758-591-3, pages 70-77. DOI: 10.5220/0011299900003289

in Bibtex Style

@conference{sigmap22,
author={Giuseppe Puglisi and Daniele Di Mauro and Antonino Furnari and Luigi Gulino and Giovanni Farinella},
title={Visual RSSI Fingerprinting for Radio-based Indoor Localization},
booktitle={Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP,},
year={2022},
pages={70-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011299900003289},
isbn={978-989-758-591-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP,
TI - Visual RSSI Fingerprinting for Radio-based Indoor Localization
SN - 978-989-758-591-3
AU - Puglisi G.
AU - Di Mauro D.
AU - Furnari A.
AU - Gulino L.
AU - Farinella G.
PY - 2022
SP - 70
EP - 77
DO - 10.5220/0011299900003289