EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing and Following смотреть онлайн

The rapid rise of accessibility of unmanned aerial vehicles or drones poses a threat to general security and confidentiality. Most of the commercially available or custom-built drones are multi-rotors and are comprised of multiple propellers. Since these propellers rotate at a high speed, they are generally the fastest moving parts of an image and cannot be directly ``seen'' by a classical camera without severe motion blur. We utilize a class of sensors that are particularly suitable for such scenarios called event cameras, which have a high temporal resolution, low latency, and high dynamic range.

In this paper, we model the geometry of a propeller and use it to generate simulated events which are used to train a deep neural network called EVPropNet to detect propellers from the data of an event camera. EVPropNet directly transfers to the real world without any fine-tuning or retraining. We present two applications of our network: (a) tracking and following an unmarked drone and (b) landing on a near-hover drone. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with different propeller shapes and sizes. Our network can detect propellers at a rate of 85.1% even when 60% of the propeller is occluded and can run at up to 35Hz on a 2W power budget. To our knowledge, this is the first deep learning-based solution for detecting propellers (to detect drones). Finally, our applications also show an impressive success rate of 92% and 90% for the tracking and landing tasks respectively.



Reference:
N.J. Sanket, C.D. Singh, C. M. Parameshwara, C. Fermüller, G.C.H.E. de Croon, Y. Aloimonos
EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing and Following
Robotics: Science and Systems 2021
PDF: http://prg.cs.umd.edu/research/EVPropNet_files/EVPropNet.pdf


Project Webpage and Software:
http://prg.cs.umd.edu/EVPropNet

Affiliations:
N.J. Sanket, C.D. Singh, C. M. Parameshwara, C. Fermüller, Y. Aloimonos are with Perception and Robotics Group (PRG) at the University of Maryland, College Park.
http://prg.cs.umd.edu
G.C.H.E. de Croon is with the Micro Air Vehicle Laboratory, Delft University of Technology, Netherlands.
https://mavlab.tudelft.nl