Prediction of microunmanned aerial vehicle flight behavior from two-dimensional intensity images

Martin Rebert; Stéphane Schertzer; Martin Laurenzis

doi:10.1117/1.OE.58.5.053101

20 May 2019 Prediction of microunmanned aerial vehicle flight behavior from two-dimensional intensity images

Martin Rebert, Stéphane Schertzer, Martin Laurenzis

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Optical Engineering, Vol. 58, Issue 5, 053101 (May 2019). https://doi.org/10.1117/1.OE.58.5.053101

Abstract

The increasing number of microunmanned aerial vehicles (MUAVs) is a rising risk for personal privacy and security of sensitive areas. Owing to the highly agile maneuverability and small cross section of the MUAV, effective countermeasures (CMs) are hard to deploy, especially when a certain temporal delay occurs between the localization and the CM effect. Here, a reliable prediction of the MUAV flight behavior can increase the effectiveness of CMs. We propose a pose estimation approach to derive the three-dimensional (3-D) flight path from a stream of two-dimensional intensity images. The pose estimation in a single image results in an estimation of the current position and orientation of the quadcopter in 3-D space. Combined with flight behavior model, this information is used to reconstruct the flight path and to predict the flight behavior of the MUAV. In our laboratory experiments, we obtained a standard deviation between 1 and 24 cm in a five-frame prediction of the 3-D position, depending in the actual flight behavior.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Martin Rebert, Stéphane Schertzer, and Martin Laurenzis "Prediction of microunmanned aerial vehicle flight behavior from two-dimensional intensity images," Optical Engineering 58(5), 053101 (20 May 2019). https://doi.org/10.1117/1.OE.58.5.053101

Received: 11 March 2019; Accepted: 23 April 2019; Published: 20 May 2019

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