國立虎尾科技大學 |

Language: English

Back

State estimation for robotics /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	State estimation for robotics // Timothy D. Barfoot, University of Toronto.
Author:	Barfoot, Timothy D.,
Published:	Cambridge ;Cambridge University Press, : c2024.,
Description:	xvi, 513 p. :ill. ; : 27 cm.;
Notes:	First edition published in 2017.
Subject:	Robots - Control systems. -
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:

557477
Robots
--Control systems.

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

Dewey Class. No.: 629.8/92 / B249

State estimation for robotics /
LDR:01693cam a2200181 a 4500 001 1131040
008 241204s2024 enka b 001 0 eng d
020 $a 9781009299893 (hbk.) : $c NT2930
020 $z 9781009299909 (ebook)
040 $a TL $b eng $c TL $d NFU $e AACR2
050 0 0 $a TJ211.35 $b .B37 2024
082 0 0 $a 629.8/92 $b B249 $2 23
100 1 $a Barfoot, Timothy D., $d 1973- $3 1131879
245 1 0 $a State estimation for robotics / $c Timothy D. Barfoot, University of Toronto.
250 $a 2nd ed.
260 # $a Cambridge ; $a New York : $b Cambridge University Press, $c c2024.
300 $a xvi, 513 p. : $b ill. ; $c 27 cm.
500 $a First edition published in 2017.
504 $a Includes bibliographical references and index.
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.
650 # 0 $a Robots $x Control systems. $3 557477
650 # 0 $a Observers (Control theory) $x Mathematics. $3 1131880
650 # 0 $a Lie groups. $3 527929