VLP-BERT: BERT-Enhanced IMU and Visible Light Tightly Coupled Integration Positioning System

Visible light positioning (VLP) has emerged as a promising indoor localization technology due to its high accuracy, low cost. However, it still faces challenges, such as environmental interference, signal noise, and occlusion. To address the above issues, a bidirectional encoder representations from transformer (BERT)-enhanced VLP and inertial navigation fusion positioning system is developed. First, to tackle the problem of inaccurate ranging caused by signal noise, we propose a Transformer-based network, VLP-BERT, which leverages long-sequence masking to enhance the network’s feature extraction capabilities from visible light (VL) signals. Moreover, the VLP-BERT is integrated into an autoencoder–decoder architecture for signal denoising. Second, to overcome the limitations of traditional ranging models (RMs) in complex environments, a deep learning-based centralized VLP RM is proposed. Finally, to enhance the system’s reliability under varying conditions, a tightly coupled fusion method integrating VLP with pedestrian dead reckoning (PDR) is proposed, incorporating error detection and state-constrained strategies. Extensive experimental evaluations demonstrate the effectiveness of VLP-BERT in both denoising and accurate ranging. The system was compared with nine different methods, the results show that the proposed tightly coupled approach not only achieves submeter-level accuracy but also significantly enhances the system’s robustness, even in challenging scenarios, such as signal blockage and poor signal quality.