000			02207nam a2200349 i 4500
001			CR9781316671528
003			UkCbUP
005			20240730160737.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			151204s2017||||enk o ||1 0|eng|d
020			_a9781316671528 (ebook)
020			_z9781107159396 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aTJ211.35 _b.B37 2017
082	0	0	_a629.8/9201512482 _223
100	1		_aBarfoot, Timothy D., _d1973- _eauthor. _974350
245	1	0	_aState estimation for robotics / _cTimothy D. Barfoot.
264		1	_aCambridge : _bCambridge University Press, _c2017.
300			_a1 online resource (xii, 368 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 28 Aug 2017).
520			_aA key aspect of robotics today is estimating the state, such as position and orientation, of a robot as it moves through the world. Most robots and autonomous vehicles depend on noisy data from sensors such as cameras or laser rangefinders to navigate in a three-dimensional world. This book presents common sensor models and practical advice on how to carry out state estimation for rotations and other state variables. It covers both classical state estimation methods such as the Kalman filter, as well as important modern topics such as batch estimation, the Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. The methods are demonstrated in the context of important applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Students and practitioners of robotics alike will find this a valuable resource.
650		0	_aRobots _xControl systems. _93388
650		0	_aObservers (Control theory) _xMathematics. _974351
650		0	_aLie groups. _959100
776	0	8	_iPrint version: _z9781107159396
856	4	0	_uhttps://doi.org/10.1017/9781316671528
942			_cEBK
999			_c84100 _d84100