/Long Distance Visual Ground-Based Signaling for Unmanned Aerial Vehicles

Long Distance Visual Ground-Based Signaling for Unmanned Aerial Vehicles

Volker Grabe and Stephen T. Nuske
Conference Paper, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), pp. 4976-4983, October, 2016

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

We present a long-range visual signal detection system that is suitable for an unmanned aerial vehicle to find an optical signal released at a desired landing site for the purposes of cargo delivery or rescue situations where radio signals or other communication systems are not available or the wind conditions at the landing site need to be signaled. The challenge here is to have a signal and detection system that works from long range ({textgreater}1000m) amongst ground clutter during various seasonal conditions on passive imagery. We use a smoke-grenade as a ground signal, which has the advantageous properties of being easy to carry by ground crews because of its light weight and small size, but when released has a long visual signaling range. We employ a camera system on the UAV with a visual texture feature extraction approach in a machine learning framework to classify image patches as `signal’ or `background’. We study conventional approaches and develop a visual feature descriptor that can better differentiate the appearance of the visual signal under varying conditions and, when used to train a random-forest classifier, outperforms commonly used feature descriptors. The system was rigorously and quantitatively evaluated on data collected from a camera mounted on a helicopter and flown towards a plume of signal smoke over a variety of seasons, ground conditions, weather conditions, and environments. Our system was capable of detecting the smoke cloud with both precision and recall rates greater than 0.95 from ranges between 1000m and 1500m. Further, we develop a method to estimate wind orientation and approximate wind strength by assessing the shape of the smoke signal. We present a preliminary evaluation of the wind estimation in conditions with different wind intensities and orientations relative to the approach direction.

Notes
http://www.iros2016.org/

BibTeX Reference
@conference{Grabe-2016-5620,
author = {Volker Grabe and Stephen T. Nuske},
title = {Long Distance Visual Ground-Based Signaling for Unmanned Aerial Vehicles},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016)},
year = {2016},
month = {October},
pages = {4976-4983},
}
2017-09-13T10:38:12+00:00