RailDriVE February 2019 - Data Set for Rail Vehicle Positioning Experiments
| datacite.relation.isDescribedBy | https://doi.org/10.1109/ITSC45102.2020.9294594 | |
| dc.contributor.author | Winter, Hanno | |
| dc.contributor.author | Roth, Michael | |
| dc.date.accessioned | 2021-02-15T08:56:11Z | |
| dc.date.available | 2020-03-31T10:00:00Z | |
| dc.date.available | 2020-06-04T12:38:59Z | |
| dc.date.available | 2021-02-15T08:56:11Z | |
| dc.date.created | 2021-02-15 | |
| dc.date.issued | 2021-02-15 | |
| dc.description | **Key facts** * **Fields of application:** railway positioning, sensor fusion, sensor models * **Available data:** 2x GNSS, 2x IMU, 1x odometer, 2x speed sensors, camera images * **Available reference data:** Open GNSS/IMU EKF-fusion solution (loosely coupled), Proprietary GNSS/IMU EKF- fusion solution (tightly coupled), 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](https://lrt- initiative.org/2020_05_28_lrtdatasetguidelines_v1_2/). **About** We provide a data set that can be used for various rail vehicle positioning experiments. The data were collected using the German Aerospace Center (DLR) research vehicle RailDriVE on a segment of the Braunschweig harbor railway in February 2019. Several sensors of the RailDriVE equipment and an additional self-sufficient system provided by Technische Universität Darmstadt (TU Darmstadt) were employed, including two GNSS receivers, two inertial measurement units (IMU), and several speed and distance sensors (radar, optical, odometer). Front- facing camera data has been included for documentation purposes. In order to simplify its use, some pre-processing steps were applied to the data, mainly to have common time and coordinate frames. Furthermore, example and reference positioning solutions as well as a track map have been included. The data can be used as a starting point for research work or student theses. Novel and established algorithms for many different sub-problems can be tested on the data, in order to facilitate their comparison and make results and insights more accessible. **Similar data sets** * <https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2529> * <https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2530> **Scripts** * <https://github.com/tud-rmr/railway_dataset_bs_20190222> **Companion paper** For reference to the data set in your research, please cite the companion paper: * M. Roth and H. Winter, "An Open Data Set for Rail Vehicle Positioning Experiments," 23rd International Conference on Intelligent Transportation Systems (ITSC), Sep. 2020 * BibTex: @InProceedings{RothWinter2020OpenDataSet, author = {Roth, Michael and Winter, Hanno}, title = {An Open Data Set for Rail Vehicle Positioning Experiments}, booktitle = {23rd International {IEEE} Conference on Intelligent Transportation Systems ({ITSC'20})}, doi = {10.1109/ITSC45102.2020.9294594}, year = {2020} } | en_US |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2292.3 | |
| dc.identifier.uri | https://doi.org/10.48328/tudatalib-166.3 | |
| 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 | 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 | RailDriVE February 2019 - Data Set for Rail Vehicle Positioning Experiments | en_US |
| dc.type | Dataset | en_US |
| dc.type | Software | en_US |
| dcterms.accessRights | openAccess | |
| person.identifier.orcid | 0000-0002-0429-1787 | |
| person.identifier.orcid | 0000-0002-4812-346X | |
| tuda.history.classification | Version=2016-2020;407-04 Verkehrs- und Transportsysteme, Logistik, Intelligenter und automatisierter Verkehr; | |
| tuda.unit | TUDa |
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| 2019-02-22_RaildriveFebruary2019_v3.zip | 1.81 GB | ZIP-Archivdateien |