Computer vision-based laser communication system for robust optical beam tracking and alignment.

Abstract

Free-space optical (FSO) communication is a promising technology for high-speed data transmission, but its effectiveness is highly dependent on precise beam alignment. In this work, we present a computer vision-assisted tracking system designed to maintain robust optical alignment in real time. By combining a lightweight convolutional neural network (CNN) with a Kalman filter, the system can detect the laser spot accurately and adjust the beam direction through a closed-loop feedback mechanism. Our experimental results show 98.5% tracking accuracy and reliable data transmission at 1 Gbps over distances up to 2 km. The system performs consistently in a variety of conditions, including fog, wind, motion blur, and glare. It significantly reduces bit error rates and improves signal stability compared to conventional tracking approaches. Running on an embedded Jetson Xavier NX platform, the system achieves low-latency operation and efficient power consumption, making it suitable for UAV and satellite applications. These results demonstrate the practical advantages of integrating computer vision into optical communication systems, especially where fast, accurate, and adaptive beam alignment is required. Future work will explore predictive tracking, multi-sensor fusion, and adaptive modulation to further improve performance in extreme conditions.