Hybrid Visible Light Positioning and Vision-Camera Navigation Using Illumination LEDs for Industrial Autonomous Guided Vehicles: A Modeling and Simulation Study

This study presents a hybrid Visible Light Positioning (VLP) and Vision-Camera localization framework for industrial Autonomous Guided Vehicles (AGVs). The proposed system fuses photodiode-based VLP ranging, camera-based LED geometry, and vision anchor corrections using an Unscented Kalman Filter (UKF). A simulation environment was developed under realistic Lambertian lighting conditions with ambient noise and field-of-view (FOV) constraints to evaluate accuracy and stability. The inclusion of a camera subsystem enhances the geometric consistency of VLP, providing continuous global localization even under degraded optical conditions. Experimental results show that the hybrid VLP + Camera + Vision configuration achieves a median localization error of 10.03 cm and a 90th-percentile error of 10.30 cm, representing a 77% improvement over VLP-only and a 34% gain over VLP + Vision. The system also achieves the lowest smoothness index (/ = 0.013), indicating highly stable and deterministic motion estimates. These results demonstrate that integrating vision geometry with VLP creates a scalable, RF-free, and ISO-compliant localization solution for smart-factory AGV navigation.