Christian Schlaile

Development of a GPS/INS/MAG navigation system and waypoint navigator for a VTOL UAV

Unmanned aerial vehicles (UAV) can be used for versatile surveillance and reconnaissance missions. If a UAV is capable of flying automatically on a predefined path the range of possible applications is widened significantly. This paper addresses the development of the integrated GPS/INS/MAG navigation system and a waypoint navigator for a small vertical take-off and landing (VTOL) unmanned four-rotor helicopter with a take-off weight below 1 kg. The core of the navigation system consists of low cost inertial sensors which are continuously aided with GPS, magnetometer compass, and a barometric height information. Due to the fact, that the yaw angle becomes unobservable during hovering flight, the integration with a magnetic compass is mandatory. This integration must be robust with respect to errors caused by the terrestrial magnetic field deviation and interferences from surrounding electronic devices as well as ferrite metals. The described integration concept with a Kalman filter overcomes the problem that erroneous magnetic measurements yield to an attitude error in the roll and pitch axis. The algorithm provides long-term stable navigation information even during GPS outages which is mandatory for the flight control of the UAV. In the second part of the paper the guidance algorithms are discussed in detail. These algorithms allow the UAV to operate in a semi-autonomous mode position hold as well an complete autonomous waypoint mode. In the position hold mode the helicopter maintains its position regardless of wind disturbances which ease the pilot job during hold-and-stare missions. The autonomous waypoint navigator enable the flight outside the range of vision and beyond the range of the radio link. Flight test results of the implemented modes of operation are shown.

Teaming of an UGV with a VTOL-UAV in urban environments

This paper focuses on the teaming of a small unmanned ground vehicle (UGV) with an unmanned micro serial vehicle (MAV) in order to improve the navigation solution of the UGV e. g. in the case of GPS loss. The image data that is acquired by the on-board camera of the MAV is processed in order to detect the UGV marked by a colored pattern. After the identification of the UGVpsilas location in an image, the location of the UGV with respect to a geo-coordinate system and its yaw angle are estimated by taking the MAVpsilas position and attitude into account. Finally, this information is processed in the system on-board the UGV to improve the navigation solution. The performances of the particular parts of the setup and also of the whole system are illustrated with experimental data.

Improved object geo-location in airborne camera images using tight integration of vision and navigation data

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.

Detection and tracking of objects in an image sequence captured by a VTOL-UAV

This paper focusses on the automated detection and tracking of moving objects in a camera sequence, that is provided by a small, electrically powered four-rotor helicopter in a hover-and-stare scenario. Two different algorithms for identifying independently moving areas are investigated and compared. The first approach bases on the previous compensation of the camera movement by estimation of homographies. Moving regions are extracted by robust background subtraction. The second approach bases on a dense optical flow field and needs no stabilization: Single points are identified that move not consistently with the background plane. These points are merged into objects by a cluster analysis algorithm. Furthermore, a strategy for tracking these objects over time is described including a Kalman filter. Due to several reasons, not every extracted area corresponds to an independently moving object and a heuristic rule set is used to sort artifacts out. Experimental results on in-flight images are presented and the performances of the developed algorithms are compared. Finally, first steps towards a geographic location of the tracked objects are described.