LUCOOP: Leibniz University Cooperative Perception and Urban Navigation Dataset

Abstract

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.

Jeldrik Axmann

Doctoral researcher at Institute of Cartography and Geoinformatics (IKG), Leibniz University Hannover

Main research domain is vehicle localization using LiDAR data

Rozhin Moftizadeh

Doctoral researcher at Geodetic Institute (GIH), Leibniz University Hannover

Research interests include Geodetic Data Analysis, Kalman Filter with Equality State Constraints, Particle Filter with Equality State Constraints, Information-Based Kinematic Multi-Sensor-Systems Georeferencing

Jingyao Su

Doctoral researcher of GNSS navigation

My research interests include GNSS navigation, integrity monitoring and uncertainty modeling with interval mathematics.

Qianqian Zou

Doctoral researcher at Institute of Cartography and Geoinformatics (IKG), Leibniz University Hannover

Research interests include uncertainty estimation of 3D mapping and incremental mapping using LiDAR point clouds.

Yunshuang Yuan

Doctoral researcher at Institute of Cartography and Geoinformatics (IKG), Leibniz University Hanover

Doctoral researcher at Institute of Cartography and Geoinformatics (IKG), Leibniz University Hanover

Prof. Claus Brenner

Professor in the geodesy department

the head of the Junior Research Group Automatic Methods for Fusion, Reduction and Consistent Combination of Complex Heterogeneous Geoinformation. His research interests are laser scanning, image processing, and pattern recognition.

Prof. Steffen Schön

Professor for positioning and navigation at IfE, Leibniz University Hannover

Spokesman of the DFG Research Training Group GRK2159 (i.c.sens), dean of studies Geodesy and Geoinformatics, and Liaison professor of the Studienstiftung des deutschen Volkes