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Bin-Picking: New Approaches for a Classical Problem
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Bester Preis: Fr. 6.89 (€ 7.05)¹ (vom 02.05.2019)Bin-Picking
ISBN: 9783319799636 bzw. 3319799630, vermutlich in Englisch, Springer Shop, Taschenbuch, neu.
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. Soft cover.
Bin-Picking
ISBN: 9783319265001 bzw. 3319265008, vermutlich in Englisch, Springer Shop, neu, E-Book, elektronischer Download.
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. eBook.
Bin-Picking - New Approaches for a Classical Problem
ISBN: 9783319264981 bzw. 3319264982, in Deutsch, Springer-Verlag Gmbh, gebundenes Buch, neu.
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 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. Englisch, Buch.
Bin-Picking - New Approaches for a Classical Problem
ISBN: 9783319265001 bzw. 3319265008, in Deutsch, Springer International Publishing, neu, E-Book, elektronischer Download.
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 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. Englisch, Ebook.
| Bin-Picking | Springer | Softcover reprint of the original 1st ed. 2016 | 2019
ISBN: 9783319799636 bzw. 3319799630, vermutlich in Englisch, Springer, Taschenbuch, neu.
Bin-Picking: New Approaches for a Classical Problem (Studies in Systems, Decision and Control) (2015)
ISBN: 9783319265001 bzw. 3319265008, in Englisch, 117 Seiten, Springer, neu, Erstausgabe, E-Book, elektronischer Download.
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., Kindle Edition, Ausgabe: 1st ed. 2016, Format: Kindle eBook, Label: Springer, Springer, Produktgruppe: eBooks, Publiziert: 2015-11-29, Freigegeben: 2015-11-29, Studio: Springer.
Bin-Picking: New Approaches for a Classical Problem
ISBN: 9783319799636 bzw. 3319799630, vermutlich in Englisch, Springer Nature, neu.
Dirk Buchholz, Books, Computers, Bin-Picking: New Approaches for a Classical Problem, 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.
Bin-Picking (2015)
ISBN: 9783319265001 bzw. 3319265008, in Englisch, Springer, Springer, Springer, neu, E-Book, elektronischer Download.
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 A.
Bin-Picking: New Approaches for a Classical Problem
ISBN: 3319264982 bzw. 9783319264981, in Deutsch, Springer, gebraucht.
used books,books, Bin-Picking : New Approaches for a Classical Problem, 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.
Bin-Picking
ISBN: 9783319264981 bzw. 3319264982, in Deutsch, neu.
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.