  Modern perception systems for autonomous flight are sensitive to occlusion
and have limited long-range capability, which is a key bottleneck in improving
low-altitude economic task performance. Recent research has shown that the
UAV-to-UAV (U2U) cooperative perception system has great potential to
revolutionize the autonomous flight industry. However, the lack of a
large-scale dataset is hindering progress in this area. This paper presents
U2UData, the first large-scale cooperative perception dataset for swarm UAVs
autonomous flight. The dataset was collected by three UAVs flying autonomously
in the U2USim, covering a 9 km$^2$ flight area. It comprises 315K LiDAR frames,
945K RGB and depth frames, and 2.41M annotated 3D bounding boxes for 3 classes.
It also includes brightness, temperature, humidity, smoke, and airflow values
covering all flight routes. U2USim is the first real-world mapping swarm UAVs
simulation environment. It takes Yunnan Province as the prototype and includes
4 terrains, 7 weather conditions, and 8 sensor types. U2UData introduces two
perception tasks: cooperative 3D object detection and cooperative 3D object
tracking. This paper provides comprehensive benchmarks of recent cooperative
perception algorithms on these tasks.
