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/ .

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In this brief, we study the distance-based navigation problem of unmanned aerial vehicles (UAVs) by using a single landmark placed at an arbitrarily unknown position. To solve the problem, we propose an integrated estimation-control scheme to simultaneously accomplishes two objectives: relative localization using only distance and odometry measurements, and navigation to the desired location under bounded control input. Asymptotic convergence is obtained by invoking the discrete-time LaSalle's invariance principle in the noise-free case, and the stability under distance measurement noise is also investigated. We also validate our theoretical findings on quadcopters equipped with ultra-wideband ranging sensors and optical flow sensors in a global positioning system (GPS)-less environment.

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