Rail Vehicle Positioning Data Set: Lucy, March 2019
| dc.contributor.author | Winter, Hanno | |
| dc.date.accessioned | 2020-11-27T07:24:17Z | |
| dc.date.available | 2020-11-27T07:24:17Z | |
| dc.date.created | 2020-12-10 | |
| dc.date.issued | 2020-11-27 | |
| dc.description | **Key facts** * **Fields of application:** railway positioning, sensor fusion, sensor models * **Available data:** 1x GNSS, 1x IMU * **Rail-track characteristics:** ≈870 km (on convetional lines) * **Available reference data:** Open GNSS/IMU EKF-fusion solution (loosely coupled) * **Structure:** This data set follows the data sharing principles of the LRT (localization reference train) initiative that are available at [lrt- initiative.org](https://lrt- initiative.org/2020_05_28_lrtdatasetguidelines_v1_2/). **About** This data set can be used for rail vehicle positioning experiments. It contains measurements of an 6-DOF IMU and a GNSS receiver. The senors were mounted on a regular rail vehicle during a trip from Chemnitz (Germany, Saxony) to Neuffen (Germany, Baden-Württemberg) 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 overall length (870km, 16hrs) with continuous journey sections of over 1h and its test-track (conventional line). **Similar data sets** * <https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2292.2> * <https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2530> **Scripts** * <https://github.com/tud-rmr/railway_dataset_nt_20190311> **BibTex** @Misc{WinterRailDataSetMarch2019, title = {Rail Vehicle Positioning Data Set: Lucy, March 2019}, author = {Winter, Hanno}, doi = {10.25534/tudatalib-359}, publisher = {Technische Universität Darmstadt}, year = {2020}, } | en_US |
| dc.description.version | initial version | en_US |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2529 | |
| dc.identifier.uri | https://doi.org/10.25534/tudatalib-359 | |
| dc.language.iso | en | en_US |
| dc.rights.license | CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0) | |
| dc.subject | Railway | en_US |
| dc.subject | Positioning | en_US |
| dc.subject | Data Set | en_US |
| dc.subject | Train | en_US |
| dc.subject | Localization | en_US |
| dc.subject | GNSS | en_US |
| dc.subject | IMU | en_US |
| dc.subject | Sensor Fusion | en_US |
| dc.subject.classification | 4.41-04 | |
| dc.subject.ddc | 380 | |
| dc.title | Rail Vehicle Positioning Data Set: Lucy, March 2019 | en_US |
| dc.type | Dataset | en_US |
| dc.type | Software | en_US |
| dcterms.accessRights | openAccess | |
| person.identifier.orcid | 0000-0002-0429-1787 | |
| tuda.history.classification | Version=2016-2020;407-04 Verkehrs- und Transportsysteme, Logistik, Intelligenter und automatisierter Verkehr; |
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| 2019-03-11_LucyMarch2019_v1.zip | 5.43 GB | ZIP-Archivdateien |