000			03351nam a22005295i 4500
001			978-3-319-26500-1
003			DE-He213
005			20180206183017.0
007			cr nn 008mamaa
008			151129s2016 gw | s |||| 0|eng d
020			_a9783319265001 _9978-3-319-26500-1
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[recurso electrónico] : _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	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://dx.doi.org/10.1007/978-3-319-26500-1
912			_aZDB-2-ENG
942			_cLIBRO_ELEC
999			_c226324 _d226324