Robotics: Science and Systems XVII

EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following

Nitin J Sanket, Chahat Deep Singh, Chethan Parameshwara, Cornelia Fermüller, Guido de Croon, Yiannis Aloimonos


The rapid rise of accessibility of unmanned aerial vehicles or drones pose 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 upto 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.



    AUTHOR    = {Nitin J Sanket AND Chahat Deep Singh AND Chethan Parameshwara AND Cornelia Fermüller AND Guido {de Croon} AND Yiannis Aloimonos}, 
    TITLE     = {{EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2021}, 
    ADDRESS   = {Virtual}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2021.XVII.074}