A Robust Data-Model Dual-Drive Fusion for IMU and Visible Light Integrated Localization in Complex Changing Environments

Visible light positioning (VLP) has attracted considerable attention due to its widespread infrastructure, low energy consumption, and high precision. However, existing VLP methods often struggle to respond promptly to environment and receiver status changes, with fixed parameters throughout execution. This limitation hinders their ability to adjust system model parameters adaptively. In this article, we propose a robust data-model dual-driven tightly coupled integration of VLP and inertial navigation with uncertainty estimation to take complementary advantage of data-driven and model-driven methods. We develop a data-driven feature encoder that consists of VLP bidirectional encoder representations from transformers (VLP-BERT) and a VLP graph attention network (VLP-GAT), enabling the system to detect abnormal observations in a changing environment.The VLP-BERT is designed to effectively encode visible light signal features, while the VLP-GAT is developed to encode the correlation feature between the signal characteristics and the anchors' spatial configuration. These encoded features are then decoded into distance confidence. Subsequently, the data-driven results are incorporated into a tightly coupled integration model, allowing for the adaptive adjustment of system parameters. In addition, the receiver is inevitably subject to occlusion and slight tilting. To address these issues, we implement occlusion detection and error detection strategies based on signal-changed features. We further use the system state from the tightly coupled integration model to exclude low-accuracy observations and solve the receiver title in VLP. Extensive experiments have been conducted to validate the presented navigator and the effectiveness of VLP-BERT and VLP-GAT. Compared to existing methods, the proposed navigator achieves precise and robust navigation in complex changing environments.