17 research outputs found
Race Against the Machine: a Fully-annotated, Open-design Dataset of Autonomous and Piloted High-speed Flight
Unmanned aerial vehicles, and multi-rotors in particular, can now perform
dexterous tasks in impervious environments, from infrastructure monitoring to
emergency deliveries. Autonomous drone racing has emerged as an ideal benchmark
to develop and evaluate these capabilities. Its challenges include accurate and
robust visual-inertial odometry during aggressive maneuvers, complex
aerodynamics, and constrained computational resources. As researchers
increasingly channel their efforts into it, they also need the tools to timely
and equitably compare their results and advances. With this dataset, we want to
(i) support the development of new methods and (ii) establish quantitative
comparisons for approaches originating from the broader robotics and artificial
intelligence communities. We want to provide a one-stop resource that is
comprehensive of (i) aggressive autonomous and piloted flight, (ii)
high-resolution, high-frequency visual, inertial, and motion capture data,
(iii) commands and control inputs, (iv) multiple light settings, and (v)
corner-level labeling of drone racing gates. We also release the complete
specifications to recreate our flight platform, using commercial off-the-shelf
components and the open-source flight controller Betaflight, to democratize
drone racing research. Our dataset, open-source scripts, and drone design are
available at: https://github.com/tii-racing/drone-racing-datasetComment: 8 pages, 7 figure
Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking
Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs)
have captured the interest of both the scientific and the industrial
communities. A typical scenario consists in the use of UAVs for surveillance or
target-search missions over a wide geographical area. In this case, it is
fundamental for the command center to accurately estimate and track the
trajectories of the UAVs by exploiting their periodic state reports. In this
work, we design an ad hoc tracking system that exploits the Long Range Wide
Area Network (LoRaWAN) standard for communication and an extended version of
the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone
movements in a 3D environment. Simulation results on a publicly available
dataset show that our system can reliably estimate the position and trajectory
of a UAV, significantly outperforming baseline tracking approaches.Comment: 6 pages, 6 figures, in review for IEEE WISARN 2020 (INFOCOM WORKSHOP)
2020 : IEEE WiSARN 2020 (INFOCOM WORKSHOP) 2020: 13th International Workshop
on Wireless Sensor, Robot and UAV Network
Development and evaluation of a dynamically scaled testbed aircraft for a visual inertial odometry dataset
In this thesis we describe the design, manufacturing, and testing of a dynamically scaled aircraft, which is a scaled model of a general aviation vehicle that dynamically behaves in a similar manner as the full-scale aircraft. This scaled model (Cirrus SR22T) is to serve as a testbed for both Distributed Electric Propulsion (DEP) aircraft research and for Visual Inertial Odometry (VIO) research. The aircraft is used as a baseline to compare with the DEP aircraft, to draw conclusion regarding the effect of changing to a DEP configuration, and to provide a way to measure the effect that a DEP configuration would have on a full-scale aircraft. The aircraft is also used to collect data from various onboard sensors to provide a data set for the VIO research community to use