Language:
English
日文
簡体中文
繁體中文
Help
Login
Back
to Search results for
[ author_sort:"buchholz, dirk." ]
Switch To:
Labeled
|
MARC Mode
|
ISBD
Bin-picking[electronic resource] :ne...
~
Buchholz, Dirk.
Bin-picking[electronic resource] :new approaches for a classical problem /
Record Type:
Language materials, printed : Monograph/item
[NT 15000414]:
621
Title/Author:
Bin-picking : new approaches for a classical problem // by Dirk Buchholz.
Author:
Buchholz, Dirk.
Published:
Cham : : Springer International Publishing :, 2016.
Description:
xv, 117 p. : : ill., digital ;; 24 cm.
Contained By:
Springer eBooks
Subject:
Mechatronics.
Subject:
Robot vision.
Subject:
Detectors.
Subject:
Engineering.
Subject:
Computational Intelligence.
Subject:
Robotics and Automation.
Subject:
Image Processing and Computer Vision.
Subject:
Artificial Intelligence (incl. Robotics)
ISBN:
9783319265001
ISBN:
9783319264981
[NT 15000228]:
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.
[NT 15000229]:
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.
Online resource:
http://dx.doi.org/10.1007/978-3-319-26500-1
Bin-picking[electronic resource] :new approaches for a classical problem /
Buchholz, Dirk.
Bin-picking
new approaches for a classical problem /[electronic resource] :by Dirk Buchholz. - Cham :Springer International Publishing :2016. - xv, 117 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.442198-4182 ;. - Studies in systems, decision and control ;v.7..
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:
455721
Mechatronics.
LC Class. No.: TJ163.12
Dewey Class. No.: 621
Bin-picking[electronic resource] :new approaches for a classical problem /
LDR
:02486nam a2200325 a 4500
001
454996
003
DE-He213
005
20160722132740.0
006
m d
007
cr nn 008maaau
008
161227s2016 gw s 0 eng d
020
$a
9783319265001
$q
(electronic bk.)
020
$a
9783319264981
$q
(paper)
024
7
$a
10.1007/978-3-319-26500-1
$2
doi
035
$a
978-3-319-26500-1
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
TJ163.12
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
082
0 4
$a
621
$2
23
090
$a
TJ163.12
$b
.B919 2016
100
1
$a
Buchholz, Dirk.
$3
652968
245
1 0
$a
Bin-picking
$h
[electronic resource] :
$b
new approaches for a classical problem /
$c
by Dirk Buchholz.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2016.
300
$a
xv, 117 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Studies in systems, decision and control,
$x
2198-4182 ;
$v
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
Mechatronics.
$3
455721
650
0
$a
Robot vision.
$3
445920
650
0
$a
Detectors.
$3
183485
650
1 4
$a
Engineering.
$3
372756
650
2 4
$a
Computational Intelligence.
$3
463962
650
2 4
$a
Robotics and Automation.
$3
463885
650
2 4
$a
Image Processing and Computer Vision.
$3
463967
650
2 4
$a
Artificial Intelligence (incl. Robotics)
$3
463642
710
2
$a
SpringerLink (Online service)
$3
463450
773
0
$t
Springer eBooks
830
0
$a
Studies in systems, decision and control ;
$v
v.7.
$3
589058
856
4 0
$u
http://dx.doi.org/10.1007/978-3-319-26500-1
950
$a
Engineering (Springer-11647)
based on 0 review(s)
Multimedia
Multimedia file
http://dx.doi.org/10.1007/978-3-319-26500-1
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login