Optical camera communications and machine learning for indoor visible light positioning - Pereira, Celso Daniel Santos (2022)
In this dissertation, a 3D indoor visible light positioning system based on machine learning and optical camera communications is presented. The system uses LED (light-emitting diode) luminaires as reference points and a rolling shutter complementary metal-oxide semiconductor (CMOS) sensor as the receiver. The LED luminaires are modulated using On-Off Keying (OOK) with unique frequencies. You Only Look Once version 5 (YOLOv5) is used for classification and estimation of the position of each visible LED luminaire in the image. The pose of the receiver is estimated using a perspective-npoint (PnP) problem algorithm. The system is validated using a real-world sized setup containing eight LED luminaires, and achieved an average positioning error of 3.5 cm. The average time to compute the camera pose is approximately 52 ms, which makes it suitable for real-time positioning. To the best of our knowledge, this is the first application of the YOLOv5 algorithm in the field of VLP for indoor environments.