This paper presents a novel navigation system for aiding a classic inertial navigation system with a Vehicle Dynamics Model in a quadrotor application. The navigation system is based on the previously presented Unified Model technique for optimal fusion of the two prediction models. The key point of the approach in this paper is that the required modeling and implementation effort is minimized by incorporating a translational dynamics model only. Even though no rotational vehicle dynamics are modeled, it is shown that the filter is able to estimate roll and pitch angles and even IMU biases with bounded errors. Indoor and outdoor flight experiments confirm that all navigation states except heading angle are significantly improved compared to the single systems. For the first time, this demonstrates in real flight experiments that model‐aided navigation allows estimating a full navigation solution for several minutes even without any aiding sensors. Copyright © 2014 Institute of Navigation.

In this paper, the development of a vision based system for a small-scale VTOL-MAV is presented. The on-board GPS/INS navigation system is augmented by further sensors in order to allow for an autonomous waypoint mode. Especially in urban environments the GPS signal quality is disturbed by shading and multipath propagation. The investigated vision system based on algorithms analyzing the optical flow is essential to enable the helicopter to reliably hover even in these scenarios. Due to the integration of the vision based navigation information into the navigation filter, GPS signal outages can be bridged. The necessary height above ground information is estimated from the relative altitude change given by the barometric altimeter and the optical flow.

This paper focuses on the real time implementation of cooperative navigation aiding for a small unmanned aerial vehicle (UAV) based on vision systems on board unmanned ground vehicles (UGV). In urban environments the signal quality of global satellite navigation systems (GNSS) often is bad or signals are even lost, so that the navigation solution of a UAV is affected. Especially in such situations the UAV's geo-referenced position has to be known to ensure a safe guidance. A team of UGVs can overcome the problem of no GNSS position solution by the detection and tracking of the UAV in its on board images and the subsequent geo-localization. The determination of the UAV's geo-referenced position can be achieved with several algorithms, depending on the number of UGVs. To integrate these position measurements in the UAV's on board navigation computer the quality of the detection and geo-localization has to be estimated. All parts are implemented on embedded PC platforms. To allow real time usage on board the UGVs special algorithms have to be used for the most tasks, as image processing needs high computational power. The particular parts of this setup as well as the whole system are tested with experimental data.