State Estimation for Robotics (Hardcover)

Language: English

Published by Cambridge University Press, Cambridge, 2024

1009299891 / 9781009299893

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Hardcover. 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. This book is intended for students and practitioners of robotics working with noisy sensor data to estimate state variables. New edition highlights include a new chapter on variational inference and new sections on adaptive covariance estimation and on inertial navigation as well as a primer on matrix calculus. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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Title
State Estimation for Robotics (Hardcover)
Author
Timothy D. Barfoot
Publisher
Cambridge University Press, Cambridge
Publication year
2024
Condition
new
Binding
Hardcover
Language
English
ISBN 10
1009299891
ISBN 13
9781009299893
Edition
2nd Edition

CitiRetail

Stevenage, United Kingdom

5-star seller

AbeBooks seller since June 29, 2022

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