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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 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aBuchholz, Dirk.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960636
245 1 0 _aBin-Picking
_h[electronic resource] :
_bNew Approaches for a Classical Problem /
_cby Dirk Buchholz.
250 _a1st ed. 2016.
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,
_x2198-4190 ;
_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 _aComputational intelligence.
_97716
650 0 _aControl engineering.
_931970
650 0 _aRobotics.
_92393
650 0 _aAutomation.
_92392
650 0 _aComputer vision.
_960637
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aControl, Robotics, Automation.
_931971
650 2 4 _aComputer Vision.
_960638
650 2 4 _aArtificial Intelligence.
_93407
710 2 _aSpringerLink (Online service)
_960639
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319264981
776 0 8 _iPrinted edition:
_z9783319264998
776 0 8 _iPrinted edition:
_z9783319799636
830 0 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v44
_960640
856 4 0 _uhttps://doi.org/10.1007/978-3-319-26500-1
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80591
_d80591