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EU Long-term Dataset with Multiple Sensors for Autonomous Driving

--- Collected by our very own UTBM robocar ---

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Description

This dataset was collected with our robocar (in human driving mode of course), equipped up to eleven heterogeneous sensors, in the downtown (for long-term data) and a suburb (for roundabout data) of Montbéliard in France. The vehicle speed was limited to 50 km/h following the French traffic rules. For the long-term data, the driving distance is about 5.0 km (containing a small and a big road loop for loop-closure purpose) and the length of recorded data is about 16 minutes for each collection round. In addition to the typical eastern French city, users can feel the daily and seasonal changes of the city. For a quick overview, please refer to the following video.

   

For the roundabout data, the driving distance is about 4.2 km (containing 10 roundabouts with various sizes) and the length of recorded data is about 12 minutes.

Contributions

This dataset provides:

  1. Robot Operating System (ROS) rosbag files recording ground truth from two stereo cameras (a Bumblebee XB3 and a Bumblebee2), two Velodyne HDL-32E lidars, two Pixelink PL-B742F cameras with fisheye lens, an ibeo LUX 4L lidar, a Continental ARS 308 radar, a SICK LMS100-10000 laser rangefinder, a Magellan ProFlex 500 GNSS receiver with an RTK base station, and an Xsens MTi-28A53G25 IMU;
  2. Data covering day, night, week, and season. As it captures daily and seasonal changes, this dataset is especially suitable for long-term autonomy research;
  3. Intensive data for roundabout challenge, a very common road condition in France as well as in other European countries, not easy to handle even for humans.
  4. Baselines based on relevant state-of-the-art methods, for the visual (monocular and stereo) and lidar odometry benchmarking (with ground-truth trajectories recorded by GPS/RTK).

Citation

If you publish work based on, or using, this dataset, we would appreciate citations to the following:

@article{utbm_robocar_dataset,
   author = {Zhi Yan and Li Sun and Tomas Krajnik and Yassine Ruichek},
   title = {{EU} Long-term Dataset with Multiple Sensors for Autonomous Driving},
   journal = {CoRR},
   volume = {abs/1909.03330},
   year = {2019},
   url = {http://arxiv.org/abs/1909.03330},
   archivePrefix = {arXiv},
   eprint = {1909.03330}
}

Recording platform

sensors.png    sensors.png

Our design mainly adheres to the following two principles: 1) strengthen the visual scope as much as possible, and 2) maximize the overlapping area perceived by multiple sensors. In particular:

For more details, please refer to our paper (in submission).

Challenges

Many new research challenges have been introduced in this dataset, such as:

sloping_road.jpg shared_zone.jpg diversion.jpg roundabout.jpg night.jpg
sloping road shared zone construction bypass roundabout night
snow.jpg right_overtaking.jpg crossing.jpg pigeon.jpg police.jpg
snow right overtaking* crossing pigeon police
*Aggressive driving / rule breaking behavior

Downloads

Please note that all rosbags are compressed, please decompress them as needed.

Long-term data:
Date Local Time (Paris) Sensors Raw data
2018-05-02 (Wed, evening) 20:40 - 20:54 (14m30s) 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK rosbag
2018-05-02 (Wed, night) 21:28 - 21:42 (13m55s) 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK / Bumblebee XB3 rosbag (images)/
2018-07-13 (Fri, sunny) 14:16 - 14:33 (16m59s) 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK rosbag
2018-07-16 (Mon, sunny) 16:10 - 16:26 (15m59s) 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK rosbag
2018-07-17 (Tue, sunny) 15:40 - 15:56 (15m59s) 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK rosbag
2018-07-18 (Wed, sunny) 15:04 - 15:21 (16m39s) 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK rosbag+
2018-07-19 (Thu, sunny) 16:15 - 16:31 (15m26s) 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK rosbag-
2018-07-20 (Fri, cloudy) 14:35 - 14:51 (16m45s) 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK rosbag
2019-01-10 (Fri, snow) 09:06 - 09:17 (10m59s) 1 × Velodyne / ibeo / SICK / IMU rosbag*
2019-01-31 (Fri, snow) 08:54 - 09:10 (15m59s) 1 × Velodyne / ibeo / SICK / IMU / GPS rosbag*
2019-04-18 (Thu, sunny) 11:07 - 11:22 (14m55s) 1 × Velodyne / ibeo / SICK / IMU / GPS-RTK / radar rosbag
+Only partial GPS-RTK data.
-Best data quality, recommended for evaluation.
*Only part of the itinerary recorded due to adverse weather conditions.
/You might want to add_header_time_offset.py
Roundabout data:
Date Local Time (Paris) Sensors Raw data
2019-04-12 (Fri, cloudy) 18:14 - 18:26 (12m10s) 1 × Velodyne / ibeo / SICK / IMU / GPS-RTK / radar rosbag
2019-04-18 (Thu, sunny) 12:03 - 12:15 (11m59s) 1 × Velodyne / ibeo / SICK / IMU / GPS-RTK / radar rosbag

Please note that, 1) it is a growing dataset and will be incrementally published, and 2) images will be incrementally available after processing to meet the GDPR requirements.

How to play

roslaunch utbm_dataset_play.launch bag:=path_to_your_rosbag

By using the provided launch file, you will have:

  1. Point cloud conversions for the Velodyne lidars, and without any spurious data (caused by blockage or reflections from the opposing sensors).
  2. The output of the object tracking functionality of the ibeo LUX lidar.
  3. A complete and aligned tf tree.

Baselines

https://github.com/epan-utbm/utbm_robocar_dataset

License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2018 Zhi Yan, Li Sun, Tomas Krajnik, and Yassine Ruichek.

Funding

This work was supported by the Quality Research Bonus (BQR) of the University of Technology of Belfort-Montbéliard (UTBM), the Contrat de Plan État-Région (CPER) 2015-2020 Mobilitech, and the PHC Barrande programme under grant agreement No. 40682ZH (3L4AV).

Sponsors

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Acknowledgment

The authors would like to thank Abdeljalil Abbas-Turki, Olivier Lamotte, Jocelyn Buisson, and Fahad Lateef for their help in building the dataset, and the Lincoln Centre for Autonomous Systems (L-CAS) for hosting the dataset.