Authors:
- Presents three full approaches to solve the bin-picking problem: based on normal maps, using depth maps, and based on point clouds in combination with depth maps
- Explains the three approaches in detail and in connection to each other, while individual chapters are also understandable on their own
- Serves as introduction to bin-picking and pose estimation
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 44)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this book
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, surfacenormal 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.
Authors and Affiliations
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Braunschweig, Germany
Dirk Buchholz
Bibliographic Information
Book Title: Bin-Picking
Book Subtitle: New Approaches for a Classical Problem
Authors: Dirk Buchholz
Series Title: Studies in Systems, Decision and Control
DOI: https://doi.org/10.1007/978-3-319-26500-1
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-26498-1Published: 04 December 2015
Softcover ISBN: 978-3-319-79963-6Published: 29 March 2019
eBook ISBN: 978-3-319-26500-1Published: 29 November 2015
Series ISSN: 2198-4182
Series E-ISSN: 2198-4190
Edition Number: 1
Number of Pages: XV, 117
Number of Illustrations: 40 b/w illustrations, 23 illustrations in colour
Topics: Computational Intelligence, Robotics and Automation, Image Processing and Computer Vision, Artificial Intelligence