In recent years, autonomous robots have become ubiquitous in research and daily life. Among many factors, public datasets play an important role in the progress of this field, as they waive the tall order of initial investment in hardware and manpower. However, for research on autonomous aerial systems, there appears to be a relative lack of public datasets on par with those used for autonomous driving and ground robots. Thus, to fill in this gap, we conduct a data collection exercise on an aerial platform equipped with an extensive and unique set of sensors: two 3D lidars, two hardware-synchronized global-shutter cameras, multiple Inertial Measurement Units (IMUs), and especially, multiple Ultra-wideband (UWB) ranging units. The comprehensive sensor suite resembles that of an autonomous driving car, but features distinct and challenging characteristics of aerial operations. We record multiple datasets in several challenging indoor and outdoor conditions. Calibration results and ground truth from a high-accuracy laser tracker are also included in each package. All resources can be accessed via our webpage https://ntu-aris.github.io/ntu_viral_ dataset/.

近年来，自主机器人在研究和日常生活中无处不在。在众多因素中，公共数据集在该领域的发展中发挥着重要作用，因为它们免去了对硬件和人力的初始投资的高要求。然而，对于自主空中系统的研究，与用于自主驾驶和地面机器人的公共数据集相比，似乎相对缺乏公共数据集。因此，为了填补这一空白，我们在配备了大量独特传感器的空中平台上进行了数据收集练习：两个 3D 激光雷达、两个硬件同步的全局快门相机、多个惯性测量单元 (IMU) 和尤其是多个超宽带 (UWB) 测距单元。综合传感器套件类似于自动驾驶汽车，但具有独特和具有挑战性的空中作战特征。我们在几个具有挑战性的室内和室外条件下记录了多个数据集。每个包中还包含来自高精度激光跟踪器的校准结果和地面实况。所有资源都可以通过我们的网页 https://ntu-aris.github.io/ntu_viral_ dataset/ 访问。