DIO-VL: Deep Learning-Based Inertial Odometry and Visible Light Fusion Localization

Visible Light Positioning (VLP) has attracted significant attention due to its low cost, low power consumption and high accuracy. However, challenges such as noise interference and limited coverage still affect the system’s performance. On the other hand, Inertial Measurement Units (IMUs) can provide position measurements that are unaffected by environmental factors, but traditional methods tend to diverge over time. To solve these issues, a method for robust fusion positioning of a deep learning-enhanced pedestrian trolley odometer and VLP has been proposed. Firstly, a deep learning-based inertial odometry (DIO) is introduced to capture gyroscope noise and hidden motion features, which can achieve precise displacement estimation within a specified window size. Secondly, a tightly coupled fusion filter that integrates IMU-based odometry with received signal strength (RSS)-based VLP is designed, it significantly enhances the system’s robustness under conditions of signal sparsity and serious signal noise interference. Finally, a method for calculating the observation error covariance based on RSS that considers measurement uncertainty has been proposed, it ensures that the system remains robust even in the presence of weak signal strength or environmental noise. Experimental evaluations demonstrate that the proposed DIO improves accuracy by 33.4% compared to existing methods, with only an 0.76% increase in average computation time. Furthermore, compared to the DIO, VLP, and particle filter fusion systems, the proposed fusion-based localization system achieves accuracy improvements of 78.1%, 23.4%, and 9.6%, respectively, while maintaining significantly lower average computational time than the particle filter system. These results indicate that the proposed fusion method significantly outperforms existing techniques and effectively addresses the limitations of individual positioning methods.