AerialDEM geolocalization for GPSdenied UAS navigation Accelerated by the proliferation of small, affordable, and lightweight electronically scanning radar systems as well as advances in Unmanned Aircraft System (UAS) technology, GeoRegistered Radar Returns data are becoming an incredible source for geolocalization in GPSdenied UAS navigation. Most existing approaches match aerial images to prestored Digital Elevation Models (DEMs) through 3D terrain reconstruction or GPUbased terrain rendering techniques. However, these reconstruction or rendering processes are themselves errorprone and timeconsuming, which further decrease UAS navigation accuracy. In this work, we propose a novel geolocalization approach by directly matching aerial images to DEMs. Inspired by success of deep learning in face recognitionverification, we develop a tripletranking network to embed aerial images and DEMs into the same lowdimensional feature space, where matching AerialDEM are near one another and mismatched AerialDEM are far apart. To create largescale training dataset, we design an efficient terrain generation approach using perpixel displacement mapping technique. This approach augments aerial datasets by simulating visual appearances of terrain under different lighting conditions. Experiments are conducted to show the effectiveness of our deep network in finding matches between aerial images and DEMs.

UAV Autonomous Localization using MacroFeatures Matching with a CAD Model Research in the field of autonomous Unmanned Aerial Vehicles (UAVs) has significantly advanced in recent years, mainly due to their relevance in a large variety of commercial, industrial, and military applications. However, UAV navigation in GPSdenied environments continues to be a challenging problem that has been tackled in recent research through sensorbased approaches. This paper presents a novel offline, portable, realtime indoor UAV localization technique that relies on macrofeature detection and matching. The proposed system leverages the support of machine learning, traditional computer vision techniques, and preexisting knowledge of the environment. The main contribution of this work is the realtime creation of a macrofeature description vector from the UAV captured images which are simultaneously matched with an offline preexisting vector from a ComputerAided Design (CAD) model. This results in a quick UAV localization within the CAD model. The effectiveness and accuracy of the proposed system were evaluated through simulations and experimental prototype implementation. Final results reveal the algorithmxe2x80x99s low computational burden as well as its ease of deployment in GPSdenied environments.