國立虎尾科技大學 |

Bin-Picking = New Approaches for a Classical Problem /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bin-Picking/ by Dirk Buchholz.
Reminder of title:	New Approaches for a Classical Problem /
Author:	Buchholz, Dirk.
Description:	XV, 117 p. 63 illus., 23 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-26500-1
ISBN:	9783319265001

Bin-Picking = New Approaches for a Classical Problem /
Buchholz, Dirk.

Bin-PickingNew Approaches for a Classical Problem /[electronic resource] :by Dirk Buchholz. - 1st ed. 2016. - XV, 117 p. 63 illus., 23 illus. in color.online resource. - Studies in Systems, Decision and Control,442198-4182 ;. - Studies in Systems, Decision and Control,27.

Introduction – Automation and the Need for Pose Estimation -- Bin-Picking – 5 Decades of Research -- 3D Point Cloud Based Pose Estimation -- Depth Map Based Pose Estimation -- Normal Map Based Pose Estimation -- Summary and Conclusion.

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.

ISBN: 9783319265001

Standard No.: 10.1007/978-3-319-26500-1doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Bin-Picking = New Approaches for a Classical Problem /
LDR:02901nam a22004095i 4500 001 980183
003 DE-He213
005 20200706040255.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319265001 $9 978-3-319-26500-1
024 7 $a 10.1007/978-3-319-26500-1 $2 doi
035 $a 978-3-319-26500-1
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Buchholz, Dirk. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1102817
245 1 0 $a Bin-Picking $h [electronic resource] : $b New Approaches for a Classical Problem / $c by Dirk Buchholz.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XV, 117 p. 63 illus., 23 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Systems, Decision and Control, $x 2198-4182 ; $v 44
505 0 $a Introduction – Automation and the Need for Pose Estimation -- Bin-Picking – 5 Decades of Research -- 3D Point Cloud Based Pose Estimation -- Depth Map Based Pose Estimation -- Normal Map Based Pose Estimation -- Summary and Conclusion.
520 $a 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.
650 0 $a Computational intelligence. $3 568984
650 0 $a Robotics. $3 561941
650 0 $a Automation. $3 596698
650 0 $a Optical data processing. $3 639187
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Robotics and Automation. $3 782979
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Artificial Intelligence. $3 646849
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319264981
776 0 8 $i Printed edition: $z 9783319264998
776 0 8 $i Printed edition: $z 9783319799636
830 0 $a Studies in Systems, Decision and Control, $x 2198-4182 ; $v 27 $3 1254124
856 4 0 $u https://doi.org/10.1007/978-3-319-26500-1
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)