Visible Light Positioning with TinyML
Improving Data Quality and Reducing Data Collection Effort
J.A. Trzykowski (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Qing Wang – Mentor (TU Delft - Embedded Systems)
Ran Zhu – Mentor (TU Delft - Embedded Systems)
J.A. Pouwelse – Graduation committee member (TU Delft - Data-Intensive Systems)
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Abstract
Visible light positioning (VLP) systems enable indoor positioning through a deployment of light-emitting diodes (LEDs) as transmitters and photodiodes (PDs) as receivers. A promising approach in VLP involves recording the received signal strength (RSS) to construct fingerprint samples for later use in positioning. However, achieving high accuracy demands a labor-intensive data collection process.
In this study, we propose improvements to a data cleaning and augmentation pipeline. Our improvements focus on preserving more source data during cleaning and data-based LED position estimation for more reliable data augmentation. Experimental results show that our approach maintains comparable positioning accuracy while reducing data collection efforts by over 99%. Furthermore, we conduct experiments to investigate the impact of spatially irregular data collection strategies on positioning accuracy. Finally, we deploy a machine learning model on a microcontroller to demonstrate the practical feasibility of our proposed methods.