Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information.

This paper presents an algorithm for cooperative UAV navigation that exploits vision-based sensing, standalone GPS, differential GPS among antennas embarked on different flying platforms, and measurements obtained by inertial sensors and magnetometers. Unlike other cooperative navigation approaches, the developed technique is mainly aimed at improving navigation performance in outdoor environments, either in real time or off-line. The logical architecture and the main processing steps are discussed. Then, algorithms for differential GPS/vision processing and sensor fusion for navigation state estimation are introduced. Covariance analysis is used for theoretical performance assessment. The hardware system used for concept demonstration comprises a customized quadrotor and different GPS antennas and receivers, and is briefly described. Finally, first results from experimental tests are presented. In particular, the attitude solution obtained by differential GPS and vision is compared with the estimates provided by the onboard autopilot system.

This paper presents an algorithm for improving navigation performance of an Unmanned Aerial Vehicle (UAV) in GPS-challenging environments, which exploits aiding measurements from one or more cooperative UAVs flying under full GPS coverage. In particular, sensor fusion is based on an Extended Kalman Filter that integrates measurements from onboard inertial sensors and magnetometers, available GPS pseudoranges, position information from cooperative UAVs, and line-of-sight estimated by vision-based tracking. Performance evaluation is carried out considering a two-vehicle formation and using covariance propagation techniques, while experimental platforms are being integrated and will be used for proof-of-concept flight demonstration. Achieved results show that available pseudorange measurements, or proper dynamics of the UAV under GPS coverage, can compensate for the intrinsic limitations of single line-of-sight aiding and ensure a significant impact on navigation performance.

This paper presents an original approach for autonomous navigation based on RGB-D data and known 3D markers, where the basic concept is to detect and recognize the markers and then to use them for a straightforward pose estimation solution. The developed algorithms can allow a quadrotor to autonomously fly in (cooperative) GPS denied environments and/or when there is no natural or artificial illumination of the scene, by following a predetermined path consisting of successive targets having a well defined shape and/or color. Algorithms for target detection and recognition based on depth data are described which are optimized for real time use, paying particular attention to the on-board computational load. Experimental tests have been carried out by integrating a RGB-Depth sensor (ASUS Xtion Pro Live) on-board a custom-built quadrotor. First results confirm the potential of the proposed approach. The technique can be applied to different types of unmanned aerial vehicles (UAVs), as well as unmanned ground vehicles (UGVs).

This paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a GPS receiver, and a vision system, to improve its navigation performance (in real time or in post processing phase) exploiting formation flying deputies equipped with GPS receivers. The key concept is to integrate differential GPS and visual tracking information within a sensor fusion algorithm based on the Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on the filtering algorithm. Then, flight testing strategy and experimental results are presented. In particular, cooperative navigation output is compared with the estimates provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate information