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008 150903s2006 gw | o |||| 0|eng d
020 _a9783540328483
_99783540328483
024 7 _a10.1007/11418382
_2doi
035 _avtls000348521
039 9 _a201509030753
_bVLOAD
_c201404121017
_dVLOAD
_c201404090755
_dVLOAD
_y201402071030
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
100 1 _aAndrade-Cetto, Juan.
_eautor
_9330647
245 1 0 _aEnvironment Learning for Indoor Mobile Robots :
_bA Stochastic State Estimation Approach to Simultaneous Localization and Map Building /
_cby Juan Andrade-Cetto, Alberto Sanfeliu.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2006.
300 _axvI, 136 páginas 63 ilustraciones Also available online.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aSpringer Tracts in Advanced Robotics,
_x1610-7438 ;
_v23
500 _aSpringer eBooks
505 0 _aSimultaneous Localization and Map Building -- Marginal Filter Stability -- Suboptimal Filter Stability -- Unscented Transformation of Vehicle States -- Simultaneous Localization, Control and Mapping -- A: The Kalman Filter -- B: Concepts from Linear Algebra -- C: Sigma Points.
520 _aThis monograph covers theoretical aspects of simultaneous localization and map building for mobile robots, such as estimation stability, nonlinear models for the propagation of uncertainties, temporal landmark compatibility, as well as issues pertaining the coupling of control and SLAM. One of the most relevant topics covered in this monograph is the theoretical formalism of partial observability in SLAM. The authors show that the typical approach to SLAM using a Kalman filter results in marginal filter stability, making the final reconstruction estimates dependant on the initial vehicle estimates. However, by anchoring the map to a fixed landmark in the scene, they are able to attain full observability in SLAM, with reduced covariance estimates. This result earned the first author the EURON Georges Giralt Best PhD Award in its fourth edition, and has prompted the SLAM community to think in new ways to approach the mapping problem. For example, by creating local maps anchored on a landmark, or on the robot initial estimate itself, and then using geometric relations to fuse local maps globally. This monograph is appropriate as a text for an introductory estimation-theoretic approach to the SLAM problem, and as a reference book for people who work in mobile robotics research in general. Juan Andrade Cetto holds a BSEE degree from CETYS University, 1993; an MSEE degree from Purdue University, 1995; and a doctorate from the Technical University of Catalonia, 2003. He is currently with the Institut the Robòtica i Informàtica Industrial, CSIC-UPC. Alberto Sanfeliu received the BSEE and PhD degrees from the Technical University of Catalonia in 1978 and 1982, respectively. He joined the UPC faculty in 1981, and is since 1984, Professor with the Systems Engineering Department, for which he was appointed Head in 2005. Dr. Sanfeliu is also affiliated to the Institut the Robòtica i Informàtica Industrial, CSIC-UPC. His current research areas are Pattern Recognition, Computer Vision, and Robotics. He is Fellow of IAPR.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aSanfeliu, Alberto.
_eautor
_9326262
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783540327950
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/11418382
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c295914
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