Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial Vehicles

This paper develops a framework for geolocating ground-based moving targets with images taken from dual unmanned aerial vehicles (UAVs). Unlike the usual moving-target geolocation methods that rely heavily on accurate navigation state sensors or assumptions of known target’s altitude, proposed does not have same limitations and performs utilizing UAVs equipped low-quality sensors. Considering Gaussian measurement errors yaw-angle bias provided by sensors, we first propose an epipolar constraint-based corresponding-point-matching method, which enables historical data to be used estimate current position target; after that, target altitude estimation method based multiview geometry, utilizes multiple images, including finally, considering negative influence processes parameter regression, take advantage iterations among two accurately two-dimensional biases UAVs. The effectiveness practicability in this are proved simulation experiments actual flight experiments.