Maximizing Water Surface Target Localization Accuracy Under Sunlight Reflection with an Autonomous Unmanned Aerial Vehicle

Authors:
Hyukseong Kwon;Josiah Yoder;Stanley Baek;Scott Gruber;Daniel Pack
Affiliations:
Academy Center for UAS Research, United States Air Force Academy, USAF Academy, USA 80840;Academy Center for UAS Research, United States Air Force Academy, USAF Academy, USA 80840;Academy Center for UAS Research, United States Air Force Academy, USAF Academy, USA 80840;Academy Center for UAS Research, United States Air Force Academy, USAF Academy, USA 80840;Department of Electrical and Computer Engineering, University of Texas in San Antonio, San Antonio, USA 78249
Venue:
Journal of Intelligent and Robotic Systems
Year:
2014

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Abstract

Reflected sunlight can significantly impact the effectiveness of vision-based object detection and tracking algorithms, especially ones developed for an aerial platform operating over a marine environment. These algorithms often fail to detect water surface objects due to sunlight glitter or rapid course corrections of unmanned aerial vehicles (UAVs) generated by the laws of aerodynamics. In this paper, we propose a UAV path planning method that maximizes the stationary or mobile target detection likelihood during localization and tracking by minimizing the sunlight reflection influences. In order to better reduce sunlight reflection effects, an image-based sunlight reflection reception adjustment is also proposed. We validate our method using both stationary and mobile target tracking tests.