000			03928nam a22004815i 4500
001			u376264
003			SIRSI
005			20160812084404.0
007			cr nn 008mamaa
008			110725s2011 gw | s |||| 0|eng d
020			_a9783642213908 _9978-3-642-21390-8
040			_cMX-MeUAM
050		4	_aTJ210.2-211.495
050		4	_aT59.5
082	0	4	_a629.892 _223
100	1		_aMullane, John. _eauthor.
245	1	0	_aRandom Finite Sets for Robot Mapping and SLAM _h[recurso electrónico] : _bNew Concepts in Autonomous Robotic Map Representations / _cby John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXXIV, 148 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Tracts in Advanced Robotics, _x1610-7438 ; _v72
505	0		_aPart I Random Finite Sets -- Why Random Finite Sets? -- Estimation with Random Finite Sets -- Part II Random Finite Set Based Robotic Mapping -- An RFS Theoretic for Bayesian Feature-Based Robotic Mapping -- An RFS ‘Brute Force’ Formulation for Bayesian SLAM -- Rao-Blackwellised RFS Bayesian SLAM -- Extensions with RFSs in SLAM.
520			_aSimultaneous Localisation and Map (SLAM) building algorithms, which rely on random vectors to represent sensor measurements and feature maps are known to be extremely fragile in the presence of feature detection and data association uncertainty. Therefore new concepts for autonomous map representations are given in this book, based on random finite sets (RFSs). It will be shown that the RFS representation eliminates the necessity of fragile data association and map management routines. It fundamentally differs from vector based approaches since it estimates not only the spatial states of features but also the number of map features which have passed through the field(s) of view of a robot's sensor(s), an attribute which is necessary for SLAM. The book also demonstrates that in SLAM, a valid measure of map estimation error is critical. It will be shown that under an RFS-SLAM representation, a consistent metric, which gauges both feature number as well as spatial errors, can be defined. The concepts of RFS map representations are accompanied with autonomous SLAM experiments in urban and marine environments. Comparisons of RFS-SLAM with state of the art vector based methods are given, along with pseudo-code implementations of all the RFS techniques presented. John Mullane received the B.E.E. degree from University College Cork, Ireland, and Ph.D degree from Nanyang Technological University (NTU), Singapore. Ba-Ngu Vo is Winthrop Professor and Chair of Signal Processing, University of Western Australia (UWA). He received joint Bachelor degrees (Science and Elec. Eng.), UWA, and Ph.D., Curtin University. Martin Adams is Professor in autonomous robotics research, University of Chile. He holds bachelors, masters and doctoral degrees from Oxford University. Ba-Tuong Vo is Assistant Professor, UWA. He received his B.Sc, B.E and Ph.D. degrees from UWA.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650	1	4	_aEngineering.
650	2	4	_aRobotics and Automation.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aVo, Ba-Ngu. _eauthor.
700	1		_aAdams, Martin. _eauthor.
700	1		_aVo, Ba-Tuong. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642213892
830		0	_aSpringer Tracts in Advanced Robotics, _x1610-7438 ; _v72
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-21390-8
596			_a19
942			_cLIBRO_ELEC
999			_c204144 _d204144