FRVO-Mono: Feature-Based Railway Visual Odometry With Monocular Camera

We propose a monocular visual odometry method for railway localization, FRVO-Mono, which exploits different features in railway environments, such as vanishing points, track lines, and pole objects to provide multi-dimensional geometric constraints for train location estimation. A multi-view measurement function between the camera, points, lines, and objects is constructed to correct rotation and scale drift. A 3-step object association strategy is also suggested to improve objects matching accuracy, and the prior information of the camera's height is used to help restore the absolute scale. Data from four real railway scenarios with different scales and speeds are collected in collaboration with China Academy of Railway Sciences (CARS). The experimental results demonstrate the great accuracy and robustness of our approach, as well as its ability to effectively rectify the scale drift to meter level within a given distance, which makes it possible to generate more accurate positioning when combined with some other semantic information.