Visual Servoing for UAVs

Vision is in fact the richest source of information for ourself and also for outdoors Robotics, and can be considered the most complex and challenging problem in signal processing for pattern recognition. The first results using Vision in the control loop have been obtained in indoors and structured environments, in which a line or known patterns are detected and followed by a robot (Feddema & Mitchell (1989), Masutani et al. (1994)). Successful works have demonstrated that visual information can be used in tasks such as servoing and guiding, in robot manipulators and mobile robots (Conticelli et al. (1999), Mariottini et al. (2007), Kragic & Christensen (2002).) Visual Servoing is an open issue with a long way for researching and for obtaining increasingly better and more relevant results in Robotics. It combines image processing and control techniques, in such a way that the visual information is used within the control loop. The bottleneck of Visual Servoing can be considered the fact of obtaining robust and on-line visual interpretation of the environment, which can be usefully treated by control structures and algorithms. The solutions provided in Visual Servoing are typically divided into Image Based Control Techniques and Pose Based Control Techniques, depending on the kind of information provided by the vision system that determine the kind of references that have to be sent to the control structure (Hutchinson et al. (1996), Chaumette & Hutchinson (2006) and Siciliano & Khatib (2008)). Another classical division of the Visual Servoing algorithms considers the physical disposition of the visual system, yielding to eye-in-hand systems and eye-to-hand systems, that in the case of Unmanned Aerial Vehicles (UAV) can be translated as on-board visual systems (Mejias (2006)) and ground visual systems (Martinez et al. (2009)). The challenge of Visual Servoing is to be useful in outdoors and non-structured environments. For this purpose the image processing algorithms have to provide visual information that has to be robust and works in real time. UAV can therefore be considered as a challenging testbed for visual servoing, that combines the difficulties of abrupt changes in the image sequence (i.e. vibrations), outdoors operation (non-structured environments) and 3D information changes (Mejias et al. (2006)). In this chapter we give special relevance to the fact of obtaining robust visual information for the visual servoing task. In section (2).we overview the main algorithms used for visual tracking and we discuss their robustness when they are applied to image sequences taken from the UAV. In sections (3). and (4). we analyze how vision systems can perform 3D pose estimation that can be used for