Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles
This work addresses the need for accurate localization in rail networks to improve capacity, but it is incremental as it evaluates existing methods rather than proposing new ones.
The paper investigated the use of visual and visual-inertial odometry methods for precise motion estimation in rail vehicles to support moving block systems, finding that stereo visual-inertial odometry performed best in various environments with evaluation against RTK-GPS ground truth.
Today, rail vehicle localization is based on infrastructure-side Balises (beacons) together with on-board odometry to determine whether a rail segment is occupied. Such a coarse locking leads to a sub-optimal usage of the rail networks. New railway standards propose the use of moving blocks centered around the rail vehicles to increase the capacity of the network. However, this approach requires accurate and robust position and velocity estimation of all vehicles. In this work, we investigate the applicability, challenges and limitations of current visual and visual-inertial motion estimation frameworks for rail applications. An evaluation against RTK-GPS ground truth is performed on multiple datasets recorded in industrial, sub-urban, and forest environments. Our results show that stereo visual-inertial odometry has a great potential to provide a precise motion estimation because of its complementing sensor modalities and shows superior performance in challenging situations compared to other frameworks.