[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

138 Design of Vision Based Control Algorithms
It is commonly accepted that over 80% of all perceptual information being received
by human brain is perceived through eyes. It is natural for control algorithms
developers to try to utilize the information as it is done by both human and
animal brains. Vision information can be utilized not only for control a single
device but also for controlling a group of e.g. mobile robots.
Control algorithm based on vision may allow remote control of an object. One
might image a mechanical arm mimicking movements of a human arm basing
solely on vision information. Design of such concepts and systems is important for
a variety of fields like medicine, military and commercial applications. Recently
human-machine interfaces are becoming more and more important of everyday
life. Vision based systems are an important part of such systems. When a group of
people communicate via each other in harsh weather conditions when there is no
possibility to communicate via voice, gestures might be used. Systems recognizing
gestures in order to send commands to a group of UAVs might significantly improve
the quality of utilization of such systems.
An alternative approach is to use aerial vision information for controlling detected
and recognized objects on the ground. Video stream acquired from UAVs has
to be stabilized and undistorted in order to acquire precise information sufficient for
control. In order to perform distortion compensation some kind of calibration has to
be previously done. There are various types of calibration e.g. using planar or nonplanar
calibration rigs. Different type of calibration might use relative optical flow
information in order to establish calibration from motion. After radial and tangential
distortion compensation ground objects can be located and their orientation might
me estimated. Real applications require to compensate for much more challenging
factors like intensity and type of illumination or acquisition noise due to e.g. high
temperature of the video acquisition device surroundings.
Another application of vision based control algorithms is navigation of UAV in
an unknown dynamically changing environment. One might assume that geodesic
terrain map is sufficient for navigation. Unfortunately such maps might be outdated
and some tall buildings might not be included. At the same time flora is
usually also not taken into consideration during creation of such maps. Finally,
there might be some other UAVs or manned flying units in a local environment of
UAV that needs to be omitted in order to successfully perform the mission of
controlled UAV. Planning a mission using a 3D maps is a challenging problem
increasing exponentially with the resolution of the map and the number of waypoints
of UAV. Therefore there is a need to simplify the process in order to allow
navigation in a dynamically changing environments.
An important part of image processing is image stabilization. It is basically divided
into three approaches: mechanical, optical and digital stabilization. Stabilization
can be used in optoelectronic gimbal mounted on board of manned objects
or unmanned vehicles (UGVs and UAVs). Generally it is desirable to apply stabilization
to all cases when deterioration of image quality is perceived due to low
and high frequencies.
The chapter includes a number of important challenges in the fields mentioned
above. At the same time valuable suggestions and conclusions of authors are
presented. Finally, designed algorithms are presented and discussed in detail.