# Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

arXiv: https://arxiv.org/abs/1904.00936 · HTML: https://mlsanity.com/paper/1904.00936/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation](https://mlsanity.com/paper/2302.14486/) (2023)
- [Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping](https://mlsanity.com/paper/2102.08145/) (2021)
- [Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing](https://mlsanity.com/paper/2408.01716/) (2024)
- [Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14974/) (2021)
- [Local and Global Information in Obstacle Detection on Railway Tracks](https://mlsanity.com/paper/2307.15478/) (2023)
- [Superquadric Object Representation for Optimization-based Semantic SLAM](https://mlsanity.com/paper/2109.09627/) (2021)
- [The Autonomous Siemens Tram](https://mlsanity.com/paper/2102.04034/) (2021)
- [A Simple yet Effective Subway Self-positioning Method based on Aerial-view Sleeper Detection](https://mlsanity.com/paper/2410.09492/) (2024)

## Abstract (arXiv)

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.