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.
翻译:近年来,自主机器人在研究和日常生活中无处不在,在很多因素中,公共数据集对这一领域的进步起着重要作用,因为它们放弃了硬件和人力方面的大量初始投资。然而,关于自主航空系统的研究,似乎相对缺乏与自主驾驶和地面机器人所使用的数据基相匹配的公共数据集。因此,为了填补这一空白,我们在一个航空平台上开展了数据收集工作,该平台配备了广泛和独特的传感器:两台3D里达、两台硬件同步全球散射照相机、多台惰性测量仪(IMU),特别是多个超大波段(UWB)的分布单元。综合传感器套件类似于自主驾驶车,但具有独特的和具有挑战性的空中操作特征。我们记录了多个具有挑战性的室内和室外条件的多个数据集。每个成套设备中还包括两个高精确度激光追踪器的校准结果和地面真相。所有资源都可以通过我们的网页 https://ntu_aribiralb.ndata.