Visible light positioning (VLP) systems are a promising solution for indoor positioning, utilizing light-emitting diodes (LEDs) as transmitters and photodiodes (PDs) as receivers.
A received signal strength (RSS) based VLP system's accuracy is heavily dependent on the density of collected fingerprints, being a very labor-intensive process.
In this study, we focus on RSS fingerprints to achieve centimetre level positioning accuracy, while addressing the challenges of labor-intensive fingerprint collection and deployment on resource-constrained devices like the Raspberry Pi Pico microcontroller.
We found different neural network architectures using Neural Architecture Search (NAS) to optimize the VLP system, which achieve on average $12mm$ positioning error with low inference latency around $50ms$ on the Raspberry Pi Pico.