⚠ 2020-12-19: The data download links are broken due to some unknown cloud server issues, we will check it as soon as possible after our university reopens, and sorry for any inconvenience caused.

⚡ 2021-01-04: Everything is back to normal.

EU Long-term Dataset with Multiple Sensors for Autonomous Driving

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

utbm_robocar.jpg   timeline.png   itinerary_longterm.png   itinerary_roundabout.png

Description

This dataset was collected with our robocar (in human driving mode of course), equipped with eleven heterogeneous sensors, in the downtown (for long-term data) and suburban (for roundabout data) areas 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. 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 for each collection round. In addition to enjoying the typical scenery of eastern France, users can also feel the daily and seasonal changes in the city. For a quick overview, please refer to the following videos.

   

Contributions

This dataset provides:

  1. Robot Operating System (ROS) rosbag files that record environmental data from two Velodyne HDL-32E lidars, an ibeo LUX 4L lidar, a SICK LMS100-10000 laser rangefinder, a Continental ARS 308 radar, two stereo cameras (a Bumblebee XB3 and a Bumblebee2), two Pixelink PL-B742F cameras with Fujinon FE185C086HA-1 fisheye lens, a Magellan ProFlex 500 GNSS receiver with an RTK base station, and an Xsens MTi-28A53G25 IMU;
  2. Data covering day/dusk/night/week/season, ans 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, which is not easy to handle even for humans;
  4. Baseline methods especially for the lidar odometry, with ground-truth trajectory recorded by GPS-RTK.

Citation

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

@inproceedings{eu_longterm_dataset,
   author = {Zhi Yan and Li Sun and Tomas Krajnik and Yassine Ruichek},
   title = {{EU} Long-term Dataset with Multiple Sensors for Autonomous Driving},
   booktitle = {Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
   year = {2020}
}

Recording platform

sensors.png    sensors.png

Our multi-sensor platform design mainly follows 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.

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.
Camera calibration (done with camera_calibration) files are available here.

Long-term data:
Date Local Time (Paris) Sensors Image Data Non-image Data
2018-05-02 (Wed, evening) 20:40-20:54 (14'30") 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK / Bumblebee XB3 rosbag rosbag/
2018-05-02 (Wed, night) 21:28-21:42 (13'55") 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK / Bumblebee XB3 rosbag rosbag/
2018-07-13 (Fri, sunny) 14:16-14:33 (16'59") 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK / Bumblebee XB3 / Bumblebee2 rosbag rosbag
2018-07-16 (Mon, sunny) 16:10-16:26 (15'59") 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK / Bumblebee XB3 / Bumblebee2 rosbag rosbag
2018-07-17 (Tue, sunny) 15:40-15:56 (15'59") 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK / Bumblebee XB3 / Bumblebee2 rosbag rosbag
2018-07-18 (Wed, sunny) 15:04-15:21 (16'39") 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK / Bumblebee XB3 / Bumblebee2 / fisheye rosbag+ rosbag
2018-07-19 (Thu, sunny) 16:15-16:31 (15'26") 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK / Bumblebee XB3 / Bumblebee2 / fisheye rosbag- rosbag
2018-07-20 (Fri, cloudy) 14:35-14:51 (16'45") 2 × Velodyne / ibeo / SICK / IMU / GPS-RTK / Bumblebee XB3 / Bumblebee2 / fisheye rosbag rosbag
2019-01-10 (Fri, snow) 09:06-09:17 (10'59") 1 × Velodyne / ibeo / SICK / IMU / Bumblebee XB3 / Bumblebee2 / fisheye rosbag* rosbag
2019-01-31 (Fri, snow) 08:54-09:10 (15'59") 1 × Velodyne / ibeo / SICK / IMU / GPS / Bumblebee XB3 / fisheye rosbag* rosbag
2019-04-18 (Thu, sunny) 11:07-11:22 (14'55") 1 × Velodyne / ibeo / SICK / IMU / GPS-RTK / radar / Bumblebee XB3 / Bumblebee2 / fisheye rosbag 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 Image data Non-image data
2019-04-12 (Fri, cloudy) 18:14-18:26 (12'10") 1 × Velodyne / ibeo / SICK / IMU / GPS-RTK / radar / Bumblebee XB3 / Bumblebee2 / fisheye rosbag rosbag
2019-04-18 (Thu, sunny) 12:03-12:15 (11'59") 1 × Velodyne / ibeo / SICK / IMU / GPS-RTK / radar / Bumblebee XB3 / Bumblebee2 / fisheye rosbag rosbag

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

Related Work

  1. Tao Yang, Xiaofei Chang, Hang Su, Nathan Crombez, Yassine Ruichek, Tomas Krajnik, and Zhi Yan. Raindrop removal with light field image using image inpainting. IEEE Access, March 2020. [PDF | Dataset]
  2. Hongkun Zheng. Image data anonymization. CIAD Internship Report, February 2020. [PDF | Code]
  3. Li Sun, Zhi Yan, Anestis Zaganidis, Cheng Zhao, and Tom Duckett. Recurrent-OctoMap: Learning state-based map refinement for long-term semantic mapping with 3-D-lidar data. IEEE Robotics and Automation Letters, July 2018. [BibTeX | PDF | Video]

Privacy

We take privacy very seriously and handle personal data in line with the General Data Protection Regulation (GDPR) (EU) 2016/679. To this end, we used deep learning-based methods to post-process the images in order to blur face and license plate information. However, if you still find yourself or your personal belongings in the data, please contact us and we will immediately remove the corresponding information from the dataset.

License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2018-2020 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), the CZ MSMT project (No. FR-8J18FR018) / PHC Barrande project (No. 40682ZH) (3L4AV), the OP VVV funded project CZ.02.1.01/0.0/0.0/16\_019/0000765 (Research Center for Informatics), and the NVIDIA GPU grant program.

Acknowledgment

The authors would like to thank Abdeljalil Abbas-Turki, Olivier Lamotte, Jocelyn Buisson, and Fahad Lateef for their help in building the dataset, the Lincoln Centre for Autonomous Systems (L-CAS) for previously hosting the dataset, and the four reviewers of ICRA 2020 and the three reviewers of IROS 2020 in helping improve the manuscript.