Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Bin-picking = new approaches for a c...
~
SpringerLink (Online service)
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.
Published:
Cham :Springer International Publishing : : 2016.,
Description:
xv, 117 p. :ill., digital ; : 24 cm.;
Contained By:
Springer eBooks
Subject:
Mechatronics. -
Online resource:
http://dx.doi.org/10.1007/978-3-319-26500-1
ISBN:
9783319265001
Bin-picking = 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. 2. .
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:
559133
Mechatronics.
LC Class. No.: TJ163.12
Dewey Class. No.: 621
Bin-picking = new approaches for a classical problem /
LDR
:02486nam a2200325 a 4500
001
860873
003
DE-He213
005
20160722132740.0
006
m d
007
cr nn 008maaau
008
170720s2016 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
1102817
245
1 0
$a
Bin-picking
$h
[electronic resource] :
$b
new approaches for a classical problem /
$c
by Dirk Buchholz.
260
$a
Cham :
$c
2016.
$b
Springer International Publishing :
$b
Imprint: Springer,
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
559133
650
0
$a
Robot vision.
$3
678980
650
0
$a
Detectors.
$3
557332
650
1 4
$a
Engineering.
$3
561152
650
2 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 (incl. Robotics)
$3
593924
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer eBooks
830
0
$a
Studies in systems, decision and control ;
$v
v. 2.
$3
975418
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
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login