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State estimation for robotics /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	State estimation for robotics // Timothy D. Barfoot, University of Toronto.
作者:	Barfoot, Timothy D.,
出版者:	Cambridge ;Cambridge University Press, : c2024.,
面頁冊數:	xvi, 513 p. :ill. ; : 27 cm.;
附註:	First edition published in 2017.
標題:	Lie groups. -
ISBN:	9781009299893 (hbk.) :

State estimation for robotics /
Barfoot, Timothy D.,1973-

State estimation for robotics /Timothy D. Barfoot, University of Toronto. - 2nd ed. - Cambridge ;Cambridge University Press,c2024. - xvi, 513 p. :ill. ;27 cm.

First edition published in 2017.

Includes bibliographical references and index.

A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus.

ISBN: 9781009299893 (hbk.) :NT2930Subjects--Topical Terms:

527929
Lie groups.

LC Class. No.: TJ211.35 / .B37 2024

Dewey Class. No.: 629.8/92 / B249

State estimation for robotics /
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520 # $a A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus.
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