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 GPSsignal 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, GPSsignal 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.
A method for precise geo-location of objects that are observed by an airborne camera is described in this paper.
The platform for image acquisition is a micro aerial vehicle (MAV) with an integrated navigation system. From
the captured image sequences and MAV navigation data, the three-dimensional positions of objects of interest
are retrieved. Different techniques for image feature tracking are compared. Combining measurements from
multiple viewpoints in a Bundle Adjustment process yields optimal accuracy of the estimated object positions.
The robustness of the optimization is enhanced by tight integration of data from both the vision and the navigation system.