Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

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 centred...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2019-04, Vol.4 (2), p.1815-1822
Main Authors: Tschopp, Florian, Schneider, Thomas, Palmer, Andrew W., Nourani-Vatani, Navid, Cadena, Cesar, Siegwart, Roland, Nieto, Juan
Format: Article
Language:English
Subjects:
Cameras
Computer vision for transportation
Estimation
Ground truth
intelligent transportation systems
Locking
Motion estimation
Motion simulation
Odometers
Rail transportation
Rails
Robot sensing systems
Robustness
SLAM
Transportation networks
Vehicles
Visualization
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Summary: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 centred 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 real time kinematic-global position system 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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2897169