Vision-based target geolocation and optimal surveillance on an Unmanned Aerial Vehicle

A real-time computer vision algorithm for the identification and geolocation of ground targets was developed and implemented on the Penn State University / Applied Research Laboratory Unmanned Aerial Vehicle (PSU/ARL UAV) system. The geolocation data is filtered using a linear Kalman filter, which provides a smoothed estimate of target location and target velocity. The vision processing routine and estimator are coupled with an onboard path planning algorithm that optimizes the vehicle trajectory to maximize surveillance coverage of the targets. The vision processing and estimation routines were flight tested onboard a UAV system with a human pilot-in-the-loop. It was found that GPS latency had a significant effect on the geolocation error, and performance was significantly improved when using latency compensation. The combined target geolocation and path planning system was tested on the ground using a hardware-in-the-loop simulation, and resulted successful tracking and observation of a fixed target. Timing results showed that is is feasible to implement total system in real time.