This bookis 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 aneasy task for humans, but its automation is very complex. In this book threedifferent approaches to solve the bin-picking problem are described, showinghow modern sensors can be used for efficient bin-picking as well as how classicsensor concepts can be applied for novel bin-picking techniques. 3D pointclouds are firstly used as basis, employing the known Random Sample Matchingalgorithm paired with a very efficient depth map based collision avoidancemechanism resulting in a very robust bin-picking approach. Reducing thecomplexity 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 andresults in real time data analysis. Combined with force/torque and accelerationsensors, a near time optimal bin-picking system emerges. Lastly, surface normalmaps are employed as a basis for pose estimation. In contrast to knownapproaches, the normal maps are not used for 3D data computation but directlyfor the object localization problem, enabling the application of a new class ofsensors for bin-picking.
This bookis 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 aneasy task for humans, but its automation is very complex. In this book threedifferent approaches to solve the bin-picking problem are described, showinghow modern sensors can be used for efficient bin-picking as well as how classicsensor concepts can be applied for novel bin-picking techniques. 3D pointclouds are firstly used as basis, employing the known Random Sample Matchingalgorithm paired with a very efficient depth map based collision avoidancemechanism resulting in a very robust bin-picking approach. Reducing thecomplexity 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 andresults in real time data analysis. Combined with force/torque and accelerationsensors, a near time optimal bin-picking system emerges. Lastly, surface normalmaps are employed as a basis for pose estimation. In contrast to knownapproaches, the normal maps are not used for 3D data computation but directlyfor the object localization problem, enabling the application of a new class ofsensors for bin-picking.
Erlebe die Fülle und die unermessliche Weite Deines höheren Selbst in allen Bereichen des Seins.Mit Hilfe der Engel und Lichtwesen erfahre Dich selbst und die verschiedenen Frequenzen und...
Dylan is always finding ways to be more creative.Who knew household tasks and routines could be so fun? Dylan's curiosity and drive leave his family making simple changes that lead to enjoyable...
Tucked deep within the heart of everyone is a greatness that is locked away. This greatness commands praise to God as its final result. You have known it your entire life; you just didn’t know all...
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