Title :
A multiple UAV system for vision-based search and localization
Author :
Tisdale, John ; Ryan, Allison ; Kim, Zu ; Törnqvist, David ; Hedrick, J. Karl
Author_Institution :
Dept. of Mech. Eng., Univ. of California, Berkeley, CA
Abstract :
The contribution of this paper is an experimentally verified real-time algorithm for combined probabilistic search and track using multiple unmanned aerial vehicles (UAVs). Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using fixed downward-looking cameras. These sensors are modeled to include vehicle state uncertainty and produce an estimate update regardless of whether the target is detected in the frame or not. This allows for a single framework for searching or tracking, and requires non-linear representations of the target position probability density function (PDF) and the sensor model. While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter solution has also been examined. Multi-aircraft flight experiments demonstrate vision-based localization of a stationary target with estimated error co- variance on the order of meters. This capability for real-time distributed estimation will be a necessary component for future research in information-theoretic control.
Keywords :
Bayes methods; aerospace control; aircraft; cameras; particle filtering (numerical methods); probability; remotely operated vehicles; sensor fusion; Bayesian estimation; distributed data fusion; downward-looking cameras; error co-variance estimation; flight demonstration; grid-based system; information-theoretic control; multiaircraft flight experiment; particle filter; probability density function; real-time algorithm; unmanned aerial vehicle; vision-based localization; vision-based search; Bayesian methods; Cameras; Particle filters; Probability density function; Sensor fusion; State estimation; Target tracking; Uncertainty; Unmanned aerial vehicles; Vehicle detection;
Conference_Titel :
American Control Conference, 2008
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4244-2078-0
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2008.4586784