GAN and semi-supervised learning-based indoor visible light localization system for image sensors

Aiming at the visible light localization system in the fingerprint acquisition link, which is time-consuming and inefficient, this paper proposes a visible light localization system based on generative adversarial network and semi-supervised learning to realize high-efficiency fingerprint acquisition and high-precision localization. Firstly, the image of the lamp is captured using a CMOS smartphone, and the fingerprint database is constructed by feature extraction of the feature image of the lamp using image processing technology, which can increase the diversity and richness of the fingerprint database; secondly, the unlabeled pseudo-fingerprint data is generated by GAN network, which expands the dataset, thus reduces the cumbersomeness of fingerprint acquisition and improves the efficiency of fingerprint acquisition; lastly, the real fingerprint data and pseudo-fingerprint data are mixed with semi-supervised training of CNN models for location prediction. The experimental results show that in the experimental space of 2.25m×1.65m×3m, only 1564 labeled fingerprint data are collected in this paper, and the achieved average error of localization is 2.48cm. among them, the probability of error less than 5cm accounts for 90%, which meets the indoor localization requirements.