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Visual servoing is a popular technique for performing
robotic tasks like relative positioning or target
tracking.
Classic visual servoing assumes that visual features
can be extracted from camera images of the target
object or scene.
However, what happens when the target object is
uniform? Can a robot position itself with respect to
a white wall or avoiding a non-textured object using
just visual feedback? What happens when the scenario
is complex or a priori unknown like a region of the
sea floor? In cases like these, visual servoing
assumptions fail. This book studies the use of
structured light to provide visual features. A
rigorous comparison of the most relevant structured
light patterns is provided. Then, structured light is
integrated to a visual servoing framework. This book
shows that a suitable design of the structured light
pattern is able to provide robust and unambiguous
visual features. Furthermore, the control law can be
optimised in order to obtain nice properties like
decoupling and robustness against image noise and
calibration errors.
robotic tasks like relative positioning or target
tracking.
Classic visual servoing assumes that visual features
can be extracted from camera images of the target
object or scene.
However, what happens when the target object is
uniform? Can a robot position itself with respect to
a white wall or avoiding a non-textured object using
just visual feedback? What happens when the scenario
is complex or a priori unknown like a region of the
sea floor? In cases like these, visual servoing
assumptions fail. This book studies the use of
structured light to provide visual features. A
rigorous comparison of the most relevant structured
light patterns is provided. Then, structured light is
integrated to a visual servoing framework. This book
shows that a suitable design of the structured light
pattern is able to provide robust and unambiguous
visual features. Furthermore, the control law can be
optimised in order to obtain nice properties like
decoupling and robustness against image noise and
calibration errors.