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.

Visual RSSI Fingerprinting for Radio-based Indoor Localization

Furnari, A;Farinella, GM
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.
978-989-758-591-3
RSSI
Indoor Localization
BLE Beacon
LSTM
Structure from Motion
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/540579
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