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.

MultiSensor 6DoF Localization For Aerial Robots In Complex GNSSDenied Environments The need for robots autonomously navigating in more and more complex environments has motivated intense Ru0026 D efforts in making robot pose estimation more accurate and reliable. This paper presents a multisensor multihypothesis method for robust 6DoF localization in complex environments. Robustness and accuracy requirements are addressed as follows. First, camera and LIDAR features are seamlessly integrated in the same statistical framework, benefiting from their synergies and providing robustness in scenarios with low or varying densities of LIDAR and visual features. Second, a multihypothesis approach is adopted to cope with scenario symmetries. The method has been carefully designed to operate in real time using feature and hypothesis filtering and efficient hypothesis refinement, and has been coded in a multicore implementation. The proposed method has been extensively validated for closedloop aerial robot navigation in different urban and industrial scenarios and has shown advantages over wellknown singlesensor techniques.