Items related to State Estimation for Robotics: Second Edition
Synopsis
This modern look at state estimation now covers variational inference, adaptive covariance estimation, and inertial navigation.
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About the Author
Timothy D. Barfoot is a Professor at the University of Toronto Institute for Aerospace Studies. He has been conducting research in the area of navigation of mobile robotics for over 20 years, both in industry and academia, for applications including space exploration, mining, military, and transportation. He is a Fellow of the IEEE Robotics and Automation Society.
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STATE ESTIMATION FOR ROBOTICS
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Cambridge University Press, 2024
ISBN 10: 1009299891
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State Estimation for Robotics
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State Estimation for Robotics: Second Edition
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State Estimation for Robotics: Second Edition
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Cambridge University Press (edition 2), 2024
ISBN 10: 1009299891
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State Estimation for Robotics
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State Estimation for Robotics: Second Edition
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State Estimation for Robotics: Second Edition
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Cambridge University Press 2023-12-31, 2023
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State Estimation for Robotics: Second Edition
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State Estimation for Robotics
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State Estimation for Robotics (Hardcover)
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Cambridge University Press, Cambridge, 2024
ISBN 10: 1009299891
ISBN 13: 9781009299893
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Hardcover. Condition: new. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781009299893
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