Tree based Single LED Indoor Visible Light Positioning Technique

TL;DR

This paper introduces a practical indoor visible light positioning method using a single LED and machine learning, achieving high accuracy with simulated training data and real-world testing.

Contribution

It presents a novel approach that trains models on simulated data for single LED VLP, reducing practical deployment challenges.

Findings

Mean 3D positioning error of 2.88 cm in testing.

Training with real images yields less than 1 cm accuracy.

Outperforms closest competitor with 6.26 cm error.

Abstract

Visible light positioning(VLP) has gained prominence as a highly accurate indoor positioning technique. Few techniques consider the practical limitations of implementing VLP systems for indoor positioning. These limitations range from having a single LED in the field of view(FoV) of the image sensor to not having enough images for training deep learning techniques. Practical implementation of indoor positioning techniques needs to leverage the ubiquity of smartphones, which is the case with VLP using complementary metal oxide semiconductor(CMOS) sensors. Images for VLP can be gathered only after the lights in question have been installed making it a cumbersome process. These limitations are addressed in the proposed technique, which uses simulated data of a single LED to train machine learning models and test them on actual images captured from a similar experimental setup. Such testing produced mean three dimensional(3D) positioning error of 2.88 centimeters while training with real images achieves accuracy of less than one centimeter compared to 6.26 centimeters of the closest competitor.