  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.
