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Environment Learning for Indoor Mobile Robots - Juan Andrade Cetto, Alberto Sanfeliu

Environment Learning for Indoor Mobile Robots

A Stochastic State Estimation Approach to Simultaneous Localization and Map Building
Buch | Hardcover
XVI, 136 Seiten
2006 | 2006
Springer Berlin (Verlag)
978-3-540-32795-0 (ISBN)
CHF 149,75 inkl. MwSt
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This 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.

Simultaneous 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.

Erscheint lt. Verlag 22.5.2006
Reihe/Serie Springer Tracts in Advanced Robotics
Zusatzinfo XVI, 136 p.
Verlagsort Berlin
Sprache englisch
Maße 155 x 235 mm
Gewicht 390 g
Themenwelt Informatik Grafik / Design Digitale Bildverarbeitung
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik Elektrotechnik / Energietechnik
Schlagworte learning • Mobile Robot • mobile robotics • robot • Roboter • Robotics • Robotics research • Simultaneous Localization and Mapping • SLAM • SLAM (Simultaneous Localization and Map Building) • Stochastic State Estimation
ISBN-10 3-540-32795-9 / 3540327959
ISBN-13 978-3-540-32795-0 / 9783540327950
Zustand Neuware
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