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RailDriVE February 2019 - Data Set for Rail Vehicle Positioning Experiments
| dc.contributor.author | Winter, Hanno | |
| dc.contributor.author | Roth, Michael | |
| dc.date.accessioned | 2020-02-28T08:54:50Z | |
| dc.date.available | 2020-03-31T10:00:00Z | |
| dc.date.issued | 2020-02-28 | |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2292 | |
| dc.description | <p><strong>Key facts</strong></p> <ul> <li><strong>Fields of application:</strong><br /> railway positioning, sensor fusion, sensor models</li> <li><strong>Available data:</strong><br /> 2x GNSS, 2x IMU, 1x odometer, 2x speed sensors, camera images</li> <li><strong>Available reference data:</strong><br /> Open GNSS/IMU EKF-fusion solution (loosely coupled), Proprietary GNSS/IMU EKF-fusion solution (tightly coupled), Track-Map</li> <li><strong>Structure:</strong><br /> This data set follows the data sharing principles of the LRT (localization reference train) initiative that are available at <a href="https://lrt-initiative.org/2020_05_28_lrtdatasetguidelines_v1_2/">lrt-initiative.org</a>.</li> </ul> <p><strong>About</strong></p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p><strong>Companion paper</strong></p> <p>For reference to the data set in your research, please cite the companion paper:</p> <ul> <li>M. Roth and H. Winter, "An Open Data Set for Rail Vehicle Positioning Experiments," 23rd International Conference on Intelligent Transportation Systems (ITSC), Sep. 2020</li> </ul> | en_US |
| dc.language.iso | en | en_US |
| dc.rights | Creative Commons Attribution 4.0 | |
| dc.rights.uri | 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.title | RailDriVE February 2019 - Data Set for Rail Vehicle Positioning Experiments | en_US |
| dc.type | Dataset | en_US |
| dc.type | Software | en_US |
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