GPS-Denied UAV Localization using Pre-existing Satellite Imagery

We present a method for localization of Unmanned Aerial Vehicles (UAVs) which is meant to replace an onboard GPS system in the event of a noisy or unreliable GPS signal. Our method requires only a downward-facing monocular RGB camera on the UAV, and pre-existing satellite imagery of the flight location to which the UAV imagery is compared and aligned. To overcome differences in the image capturing conditions between the satellite and UAV, such as seasonal and perspective changes, we propose the use of Convolutional Neural Network (CNN) representations trained on readily available satellite data. To increase localization accuracy, we also develop an optimization which jointly minimizes the error between adjacent UAV frames as well as the satellite map. We demonstrate how our method improves on recent systems from literature by achieving greater performance in flight environments with very few landmarks. For a GPS-denied flight at 0.2km altitude, over a flight distance of 0.85km, we achieve average localization error of less than 8 meters. We make our source code and datasets available to encourage further work on this emerging topic.