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Bin-Picking: New Approaches for a Classical Problem

Bin-Picking: New Approaches for a Classical Problem (Hardback)

Printed to order. Despatched in 2-3 weeks.
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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.

Computing & ITComputer scienceArtificial intelligenceComputing & ITComputer scienceImage processingTechnicalElectronics & communications engineeringElectronics engineeringAutomatic control engineering Publisher: Springer International Publishing AG Publication Date: 30/11/2015 ISBN-13: 9783319264981  Details: Type: Hardback Format: Books
Availability: Printed to order. Despatched in 2-3 weeks.  

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