A Continuous Autonomous Train Positioning Method Using Stereo Vision and Object Tracking

Accurate and reliable train positioning is essential for safe railway operations. However, traditional infrastructure-based methods lack the ability for autonomy and continuous positioning. To solve the problem, we propose a novel modular visual processing framework for continuous train positioning. Environmental information captured by stereo vision sensors is processed using a feature extraction algorithm to detect landmarks along the railway. The detection results are input into a dual-stream parallel processing architecture, where an anchor-based stereo matching module calculates the depth values of detected landmarks, providing essential spatial information. Simultaneously, a dynamic region of interest-based multiobject tracking module assigns unique identification codes to each landmark, facilitating the integration of landmark detection results with their corresponding depth values. This correlation allows for the tracking of changes in landmark positions and the calculation of displacement caused by train motion, leading to a more accurate estimation of the train’s location. Subsequently, the train mileage is matched against a database track map to obtain the final precise position. Field tests on Beijing Metro Line 9 and the Capital Airport Line demonstrate an average mean relative error of 0.19% and positioning accuracy within 1 m over a range of 100 m.