000			01926nam a2200277 a 4500
001			vtls000020819
003			KUKTEM
005			20251117144339.0
008			070817s2007 gw f 001 0 eng d
020			_a9783540463993
020			_a3540463992
039		9	_a201203222026 _bida _c201107131922 _dVLOAD _c200908141329 _dVLOAD _c200908141301 _dVLOAD _y200708170950 _zkam
040			_aUMP
090			_aTJ211.35 .M66 2007
100	1		_aMontemerlo, Michael
245	1	0	_aFastslam : _ba scalable method for the the simultaneous localization and mapping problem in robotics / _cMichael Montemerlo, Sebastian Thrun
260			_aBerlin : _bSpringer, _c2007
300			_a119 p. : _bill. (some col.), maps ; _c24 cm.
520			_aThis monograph describes a new family of algorithms for the simultaneous localization and mapping problem in robotics (SLAM). SLAM addresses the problem of acquiring an environment map with a roving robot, while simultaneously localizing the robot relative to this map. This problem has received enormous attention in the robotics community, reaching a peak of popularity on the occasion of the DARPA Grand Challenge in October 2005, which was won by the team headed by the authors. The FastSLAM family of algorithms applies particle filters to the SLAM Problem, which provides new insights into the data association problem that is paramount in SLAM. The FastSLAM-type algorithms have enabled robots to acquire maps of unprecedented size and accuracy in a number of robot application domains and have been successfully applied in different dynamic environments, including the solution to the problem of people tracking.
650		0	_aRobots _xControl systems
650		0	_aMobile robots
650		0	_aData transmission systems
650		0	_aCartography
700	1		_aThrun, Sebastian
999			_aVIRTUA40 _c34460 _d34466
999			_aVTLSSORT0080*0200*0201*0400*0900*1000*2450*2600*3000*5200*6500*6501*6502*6503*7000*9991