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001 978-3-319-26500-1
003 DE-He213
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007 cr nn 008mamaa
008 151129s2016 gw | s |||| 0|eng d
020 _a9783319265001
_9978-3-319-26500-1
024 7 _a10.1007/978-3-319-26500-1
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aBuchholz, Dirk.
_eauthor.
245 1 0 _aBin-Picking
_h[electronic resource] :
_bNew Approaches for a Classical Problem /
_cby Dirk Buchholz.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXV, 117 p. 63 illus., 23 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Systems, Decision and Control,
_x2198-4182 ;
_v44
505 0 _aIntroduction - 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 _aThis 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 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 0 _aComputational intelligence.
650 0 _aRobotics.
650 0 _aAutomation.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aRobotics and Automation.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319264981
830 0 _aStudies in Systems, Decision and Control,
_x2198-4182 ;
_v44
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-26500-1
912 _aZDB-2-ENG
942 _cEBK
999 _c56931
_d56931