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Accurate train positioning is essential for ensuring safety and operational efficiency in modern rail systems. Traditional methods based on trackside infrastructure or satellite signals often suffer from limited precision or high cost, especially in GNSS-denied environments. To address these challenges, this paper proposes a hybrid vision-inertial train positioning method that combines visual absolute positioning with IMU-based relative positioning. An enhanced YOLO-based object detection algorithm and an end-to-end text recognition network are employed to identify and interpret railway landmarks. The absolute position of the train is then retrieved by matching recognized text with a pre-constructed database. To achieve continuous and robust localization, a Differential Evolution Kalman Filter (DE-KF) is introduced to adaptively fuse IMU data with the vision-derived observations, dynamically tuning the process noise covariance in response to environmental variation. The proposed method was validated at the Beijing National Railway Experimental Center. Experimental results demonstrate that the system maintains positioning errors within 3.5 meters and achieves high recognition performance, with an mAP50 of 98.0%. These findings confirm the effectiveness of the proposed fusion framework for real-time, accurate, and resource-efficient train localization.
Song et al. (Wed,) studied this question.