  Recent years have seen tremendous advancements in the area of autonomous
payload delivery via unmanned aerial vehicles, or drones. However, most of
these works involve delivering the payload at a predetermined location using
its GPS coordinates. By relying on GPS coordinates for navigation, the
precision of payload delivery is restricted to the accuracy of the GPS network
and the availability and strength of the GPS connection, which may be severely
restricted by the weather condition at the time and place of operation. In this
work we describe the development of a micro-class UAV and propose a novel
navigation method that improves the accuracy of conventional navigation methods
by incorporating a deep-learning-based computer vision approach to identify and
precisely align the UAV with a target marked at the payload delivery position.
This proposed method achieves a 500% increase in average horizontal precision
over conventional GPS-based approaches.
