Spectral-Loc: Indoor Localization using Light Spectral Information

TL;DR

Spectral-Loc leverages the unique spectral distribution of indoor light, influenced by local materials and colors, to accurately determine device location without extra hardware, outperforming traditional intensity-based methods.

Contribution

We introduce Spectral-Loc, a novel indoor localization system that utilizes light spectral information from commercial sensors, enhancing accuracy without additional infrastructure.

Findings

Spectral-Loc reduces localization error compared to intensity-only methods.

Evaluation in diverse indoor spaces confirms spectral data improves accuracy.

The system can be deployed with existing spectral sensors in consumer devices.

Abstract

For indoor settings, we investigate the impact of location on the spectral distribution of the received light, i.e., the intensity of light for different wavelengths. Our investigations confirm that even under the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. By exploiting this observation, we propose Spectral-Loc, a novel indoor localization system that uses light spectral information to identify the location of the device. With spectral sensors finding their way in latest products and applications, such as white balancing in smartphone photography, Spectral-Loc can be readily deployed without requiring any additional hardware or infrastructure. We prototype Spectral-Loc using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localisation accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations over two different indoor spaces, a meeting room and a large office space, demonstrate that use of light spectral information significantly reduces the localization error for the different percentiles.