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Titlebook: Environment Learning for Indoor Mobile Robots; A Stochastic State E Juan Andrade-Cetto,Alberto Sanfeliu Book 2006 Springer-Verlag Berlin He

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發(fā)表于 2025-3-21 18:26:55 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Environment Learning for Indoor Mobile Robots
副標題A Stochastic State E
編輯Juan Andrade-Cetto,Alberto Sanfeliu
視頻videohttp://file.papertrans.cn/313/312446/312446.mp4
叢書名稱Springer Tracts in Advanced Robotics
圖書封面Titlebook: Environment Learning for Indoor Mobile Robots; A Stochastic State E Juan Andrade-Cetto,Alberto Sanfeliu Book 2006 Springer-Verlag Berlin He
描述.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 approac
出版日期Book 2006
關鍵詞Mobile Robotics; SLAM; Simultaneous Localization and Mapping; Stochastic State Estimation; learning; mobi
版次1
doihttps://doi.org/10.1007/11418382
isbn_softcover978-3-642-06931-4
isbn_ebook978-3-540-32848-3Series ISSN 1610-7438 Series E-ISSN 1610-742X
issn_series 1610-7438
copyrightSpringer-Verlag Berlin Heidelberg 2006
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1610-7438 itself, and then using geometric relations to fuse local maps globally. This monograph is appropriate as a text for an introductory estimation-theoretic approac978-3-642-06931-4978-3-540-32848-3Series ISSN 1610-7438 Series E-ISSN 1610-742X
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978-3-642-06931-4Springer-Verlag Berlin Heidelberg 2006
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Women’s Authorship in Interwar Yugoslavia Unfortunately, in SLAM, the state space constructed by appending the robot pose and the landmark locations is fully correlated; a situation that hinders full observability. Moreover, the modeling of map states as static landmarks yields a partially controllable state vector. The identification of t
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發(fā)表于 2025-3-22 23:44:48 | 只看該作者
https://doi.org/10.1057/9780230513792 state space. The diagonal elements of . corresponding to these incorruptible states will be driven to zero by the Kalman filter, and once this happens, these estimates will remain fixed and no further observations will alter their values. The dynamics of the model assume the landmarks are fixed ele
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發(fā)表于 2025-3-23 01:34:39 | 只看該作者
Ardis Hanson,Bruce Lubotsky Levinse of the EKF, is in the linear propagation of means and covariances. Vehicle and sensor models are usually of a very high nonlinear nature, and the effects of linearization required in the EKF can lead to filter divergence [53].
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發(fā)表于 2025-3-23 07:50:51 | 只看該作者
https://doi.org/10.1057/9781137455307ion. For example, by studying which vehicle maneuvers would most effectively reduce localization uncertainty [25, 78], or what maneuvers would provide the greatest reward in terms of exploration gain [31]; by incorporating visual servoing techniques [20], or by implementing a PD controller over an A
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