Representing Substantial Heading Uncertainty for Accurate Geolocation by Small UAVs

Abstract

Geolocation of a ground object of interest from live video is a common task required of small and micro unmanned aerial vehicles in surveillance and rescue applications. The small low-cost sensors these vehicles carry provide low accuracy when mapping an image coordinate to a world location. Frequently, a primary source of such inaccuracy is error in vehicle heading. Filtering methods that inadequately represent the resulting nonlinear uncertainty distributions of geolocation measurements will produce inconsistent and inaccurate estimates. This paper presents a geolocation filter with a discretized solution space that correctly handles sampled nonlinear observations. The filter achieves higher accuracy when compared to alternative linearized methods. Assessment of the improved solution accuracy for stationary objects is provided through flight experiments using a commercial human-portable fixed-wing UAV system.