Rail Vehicle Positioning Data Set: Lucy, October 2018
Description
Key facts
- Fields of application:
railway positioning, sensor fusion, sensor models - Available data:
2x GNSS, 1x IMU - Rail-track characteristics:
≈120 km on conventional and secondary line (tight curves, steep slopes, forested embankment) - Available reference data:
Open GNSS/IMU EKF-fusion solution (loosely coupled), high precision 3D track-map - Structure:
This data set follows the data sharing principles of the LRT (localization reference train) initiative that are available at lrt-initiative.org.
About
This data set can be used for rail vehicle positioning experiments. It contains measurements of an 6-DOF IMU and two GNSS receivers. The senors were mounted on a regular rail vehicle during a trip from Chemnitz (Germany, Saxony) to Schwarzenberg (Germany, Saxony) and back.
The recorded data have been pre-processed to have common time and coordinate frames. Furthermore, a simple loosely coupled GNSS/IMU positioning solution is provided which can be used as a baseline for more advanced fusion approaches.
All MATLAB scripts used to process the raw data and to calculate the GNSS/IMU positioning solution are provided within the data set.
The data can be used as a starting point for own work. Special features of this data set are its covered terrain (tight curves, steep slopes, forest embankment), its test-track (often used as test-track for new railway equipment) and the availability of a high precision 3D track-map.
Similar data sets
- https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2292.2
- https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2529
Scripts
BibTex
@Misc{WinterRailDataSetOctober2018,
title = {Rail Vehicle Positioning Data Set: Lucy, October 2018},
author = {Winter, Hanno},
doi = {10.25534/tudatalib-360},
publisher = {Technische Universität Darmstadt},
year = {2020},
}
DFG subject classification
4.41-04 Verkehrs- und Transportsysteme, Intelligenter und automatisierter VerkehrURI
https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2530https://doi.org/10.25534/tudatalib-360
Collections
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