Environment Learning for Indoor Mobile Robots: A Stochastic State Estimation

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
Subjects
Mathematics & Sciences
Publication Date
2006-06-01
Pages
136
Topic
Robotics
Narrative Type
Nonfiction
ISBN
9783540327950
Category

About this product

Product Identifiers

Publisher
Springer Berlin / Heidelberg
ISBN-10
3540327959
ISBN-13
9783540327950
eBay Product ID (ePID)
52619667

Product Key Features

Number of Pages
Xvi, 136 Pages
Publication Name
Environment Learning for Indoor Mobile Robots : A Stochastic State Estimation Approach to Simultaneous Localization and Map Building
Language
English
Publication Year
2006
Subject
Intelligence (Ai) & Semantics, Electrical, Robotics, Computer Vision & Pattern Recognition
Type
Textbook
Subject Area
Computers, Technology & Engineering
Author
Juan Andrade-Cetto, Alberto Sanfeliu
Series
Springer Tracts in Advanced Robotics Ser.
Format
Mixed Lot

Dimensions

Item Weight
14.1 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2006-921746
Dewey Edition
22
Series Volume Number
23
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
629.8/932
Table Of Content
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.
Synopsis
This monograph covers theoretical aspects of simultaneous localization and map building for mobile robots. These include 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., 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.
LC Classification Number
TJ212-225

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