LUCOOP: Leibniz University Cooperative Perception and Urban Navigation Dataset

##A real-world multi-vehicle multi-modal V2V and V2X dataset

Recently published datasets have been increasingly
comprehensive with respect to their variety of simultaneously
used sensors, traffic scenarios, environmental conditions, and
provided annotations. However, these datasets typically only
consider data collected by one independent vehicle. Hence, there
is currently a lack of comprehensive, real-world, multi-vehicle
datasets fostering research on cooperative applications such as
object detection, urban navigation, or multi-agent SLAM. In
this paper, we aim to fill this gap by introducing the novel
LUCOOP dataset, which provides time-synchronized multi-modal
data collected by three interacting measurement vehicles. The
driving scenario corresponds to a follow-up setup of multiple
rounds in an inner city triangular trajectory. Each vehicle was
equipped with a broad sensor suite including at least one LiDAR
sensor, one GNSS antenna, and up to three IMUs. Additionally,
Ultra-Wide-Band (UWB) sensors were mounted on each vehicle,
as well as statically placed along the trajectory enabling both
V2V and V2X range measurements. Furthermore, a part of the
trajectory was monitored by a total station resulting in a highly
accurate reference trajectory. The LUCOOP dataset also includes
a precise, dense 3D map point cloud, acquired simultaneously by
a mobile mapping system, as well as an LOD2 city model of
the measurement area. We provide sensor measurements in a
multi-vehicle setup for a trajectory of more than 4 km and a
time interval of more than 26 minutes, respectively. Overall, our
dataset includes more than 54,000 LiDAR frames, approximately
700,000 IMU measurements, and more than 2.5 hours of 10 Hz
GNSS raw measurements along with 1 Hz data from a reference
station. Furthermore, we provide more than 6,000 total station
measurements over a trajectory of more than 1 km and 1,874
V2V and 267 V2X UWB measurements. Additionally, we offer
3D bounding box annotations for evaluating object detection
approaches, as well as highly accurate ground truth poses for
each vehicle throughout the measurement campaign.

## Data access
**Important: Before downloading and using the data, please check the Updates.zip in the "Data and Resources" section at the bottom of this web site. There, you find updated files and annotations as well as update notes.**

* The dataset is available [here](https://data.uni-hannover.de/vault/icsens/axmann/lucoop-leibniz-university-cooperative-perception-and-urban-navigation-dataset/).
* Additional information are provided and constantly updated in our [README](https://data.uni-hannover.de/de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/efaf0db1-8859-4452-87b9-2997da2b5a69/download/lucoop_readme_21_april_23.pdf).
* The corresponding paper is available [here](https://ieeexplore.ieee.org/document/10186693).
* Cite this as: J. Axmann et al., "LUCOOP: Leibniz University Cooperative Perception and Urban Navigation Dataset," 2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage, AK, USA, 2023, pp. 1-8, doi: 10.1109/IV55152.2023.10186693.

##Preview
[](https://data.uni-hannover.de/de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/0fcf3b47-50ac-42e6-b4a0-8e1bb6a69e2f/download/teaser_video.mp4)
Source LOD2 City model: Auszug aus den Geodaten des Landesamtes für Geoinformation und Landesvermessung Niedersachsen, ©2023, [www.lgln.de](https://www.lgln.niedersachsen.de/startseite/)

![Alt text][id_lgln]
[id_lgln]: https://data.uni-hannover.de/de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/541747ed-3d6e-41c4-9046-15bba3702e3b/download/lgln_logo.png "LGLN logo"

##Sensor Setup of the three measurement vehicles

![Alt text][id]
[id]: https://data.uni-hannover.de/de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/d141d4f1-49b0-40e6-b8d9-e49f420e3627/download/vans_with_redgreen_cs_vehicle.png "Sensor Setup of the three measurement vehicles"
Sensor setup of all the three vehicles: Each vehicle is equipped with a LiDAR sensor (green), a UWB unit (orange), a GNSS antenna (purple), and a Microstrain IMU (red). Additionally, each vehicle has its unique feature: Vehicle 1 has an additional LiDAR at the trailer hitch (green) and a prism for the tracking of the total station (dark red hexagon). Vehicle 2 provides an iMAR iPRENA (yellow) and iMAR FSAS (blue) IMU, where the platform containing the IMUs is mounted inside the car (dashed box). Vehicle 3 carries the RIEGL MMS (pink). Along with the sensors and platforms, the right-handed body frame of each vehicle is also indicated.

##3D map point cloud

![Alt text][idmap]
[idmap]: https://data.uni-hannover.de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/5b6b37cf-a991-4dc4-8828-ad12755203ca/download/map_point_cloud.png "3D map point cloud"
High resolution 3D map point cloud: Different locations and details along the trajectory. Colors according to reflectance values.

##Measurement scenario

![Alt text][id1]
[id1]: https://data.uni-hannover.de/de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/6c61d297-8544-4788-bccf-7a28ccfa702a/download/scenario_with_osm_reference.png "Measurement scenario"
Driven trajectory and locations of the static sensors: The blue hexagons indicate the positions of the static UWB
sensors, the orange star represents the location of the total station, and the orange shaded area illustrates the coverage of
the total station. The route of the three measurement vehicles is shown in purple. Background map: [OpenStreetMap copyright](https://www.openstreetmap.org/copyright)

[](https://data.uni-hannover.de/de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/18837b17-1d43-4464-b4f7-35d631ca041d/download/topview_rate5.mp4)
Source LOD2 City model: Auszug aus den Geodaten des Landesamtes für Geoinformation und Landesvermessung Niedersachsen, ©2023, [www.lgln.de](https://www.lgln.niedersachsen.de/startseite/)

##Number of annotations per class (final)

![Alt text][id_ann]
[id_ann]: https://data.uni-hannover.de/de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/8b0262b9-6769-4a5d-a37e-8fcb201720ef/download/annotations.png "Number of annotations per class"

[](https://data.uni-hannover.de/de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/7713eb55-fb84-4534-835a-b535305492f5/download/perspectiveview.mp4)
Source LOD2 City model: Auszug aus den Geodaten des Landesamtes für Geoinformation und Landesvermessung Niedersachsen, ©2023, [www.lgln.de](https://www.lgln.niedersachsen.de/startseite/)

##Data structure

![Alt text][id_d]
[id_d]: https://data.uni-hannover.de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/7358ed31-9886-4c74-bec2-6868d577a880/download/data_structure.png "Data structure"

##Data format

![Alt text][id_f]
[id_f]: https://data.uni-hannover.de/de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/fc795ec2-f920-4415-aac6-6ad3be3df0a9/download/data_format.png "Data format"

##Gallery

![Alt text][id6]
[id6]: https://data.uni-hannover.de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/a1974957-5ce2-456c-9f44-9d05c5a14b16/download/vans_merged.png "Measurement vehicles"
From left to right: Van 1, van 2, van 3.

![Alt text][id5]
[id5]: https://data.uni-hannover.de/dataset/a20cf8fa-f692-40b3-9b9b-d2f7c8a1e3fe/resource/53a58500-8847-4b3c-acd4-a3ac27fc8575/download/ts_uwb_mms.png ""
From left to right: Tracking of the prism on van 1 by means of the MS60
total station, the detected prism from the view point of the MS60 total station, PulsON 440 Ultra Wide Band (UWB) sensors, RIEGL VMX-250 Mobile Mapping System.

## Acknowledgement
This measurement campaign could not have been carried
out without the help of many contributors. At this point, we
thank Yuehan Jiang (Institute for Autonomous Cyber-Physical
Systems, Hamburg), Franziska Altemeier, Ingo Neumann,
Sören Vogel, Frederic Hake (all Geodetic Institute, Hannover),
Colin Fischer (Institute of Cartography and Geoinformatics,
Hannover), Thomas Maschke, Tobias Kersten, Nina Fletling
(all Institut für Erdmessung, Hannover), Jörg Blankenbach
(Geodetic Institute, Aachen), Florian Alpen (Hydromapper
GmbH), Allison Kealy (Victorian Department of Environment,
Land, Water and Planning, Melbourne), Günther Retscher,
Jelena Gabela (both Department of Geodesy and Geoin-
formation, Wien), Wenchao Li (Solinnov Pty Ltd), Adrian
Bingham (Applied Artificial Intelligence Institute, Burwood),
and the student assistants Manuel Kramer, Khaled Ahmed,
Leonard Göttert, Dennis Mußgnug, Chengqi Zhou, and We-
icheng Zhang. Thanks to the Landesamt für Geoinformation
and Landesermessung Niedersachsen (LGLN)/Zentrale Stelle
SAPOS® for providing the virtual reference station data, in-
frastructure and reliable high quality. This project is supported
by the German Research Foundation (DFG), as part of the
Research Training Group i.c.sens, GRK 2159, ”Integrity and
Collaboration in Dynamic Sensor Networks”.

搜集汇总

背景与挑战

背景概述

LUCOOP数据集是一个真实世界的多车辆多模态V2V和V2X数据集，由三辆交互车辆在超过4公里的城市三角形轨迹上收集时间同步数据，传感器包括LiDAR、GNSS、IMU和UWB，支持车对车和车对基础设施通信。它提供了高精度参考轨迹、3D地图点云和LOD2城市模型，以及大量传感器数据（如超过54,000个LiDAR帧）和3D边界框注释，旨在促进协同感知和城市导航研究。