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This book is devoted to one of the most famous examples of
automation handling tasks - the "bin-picking" problem. To pick up
objects, scrambled in a box is an easy task for humans, but its
automation is very complex. In this book three different approaches
to solve the bin-picking problem are described, showing how modern
sensors can be used for efficient bin-picking as well as how
classic sensor concepts can be applied for novel bin-picking
techniques. 3D point clouds are firstly used as basis, employing
the known Random Sample Matching algorithm paired with a very
efficient depth map based collision avoidance mechanism resulting
in a very robust bin-picking approach. Reducing the complexity of
the sensor data, all computations are then done on depth maps. This
allows the use of 2D image analysis techniques to fulfill the tasks
and results in real time data analysis. Combined with force/torque
and acceleration sensors, a near time optimal bin-picking system
emerges. Lastly, surface normal maps are employed as a basis for
pose estimation. In contrast to known approaches, the normal maps
are not used for 3D data computation but directly for the object
localization problem, enabling the application of a new class of
sensors for bin-picking.
This book is devoted to one of the most famous examples of
automation handling tasks - the "bin-picking" problem. To pick up
objects, scrambled in a box is an easy task for humans, but its
automation is very complex. In this book three different approaches
to solve the bin-picking problem are described, showing how modern
sensors can be used for efficient bin-picking as well as how
classic sensor concepts can be applied for novel bin-picking
techniques. 3D point clouds are firstly used as basis, employing
the known Random Sample Matching algorithm paired with a very
efficient depth map based collision avoidance mechanism resulting
in a very robust bin-picking approach. Reducing the complexity of
the sensor data, all computations are then done on depth maps. This
allows the use of 2D image analysis techniques to fulfill the tasks
and results in real time data analysis. Combined with force/torque
and acceleration sensors, a near time optimal bin-picking system
emerges. Lastly, surface normal maps are employed as a basis for
pose estimation. In contrast to known approaches, the normal maps
are not used for 3D data computation but directly for the object
localization problem, enabling the application of a new class of
sensors for bin-picking.
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