State Estimation for Robotics

by Timothy D. Barfoot

Overview

A key aspect of robotics today is estimating the state, such as position and orientation, of a robot as it moves through the world. Most robots and autonomous vehicles depend on noisy data from sensors such as cameras or laser rangefinders to navigate in a three-dimensional world. This book presents common sensor models and practical advice on how to carry out state estimation for rotations and other state variables. It covers both classical state estimation methods such as the Kalman filter, as well as important modern topics such as batch estimation, the Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. The methods are demonstrated in the context of important applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Students and practitioners of robotics alike will find this a valuable resource.

Product Details

ISBN-13:	9781108506731
Publisher:	Cambridge University Press
Publication date:	07/31/2017
Sold by:	Barnes & Noble
Format:	eBook
File size:	78 MB
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About the Author

Timothy D. Barfoot is a Professor at the University of Toronto Institute for Aerospace Studies (UTIAS). He has been conducting research in the area of navigation of mobile robotics for over fifteen years, both in industry and academia, for applications including space exploration, mining, military, and transportation. He has made contributions in the area of localization, mapping, planning, and control. He sits on the editorial boards of the International Journal of Robotics Research and the Journal of Field Robotics, and was the General Chair of Field and Service Robotics 2015, which was held in Toronto.

Table of Contents

1. Introduction; Part I. Estimation Machinery: 2. Primer on probability theory; 3. Linear-Gaussian estimation; 4. Nonlinear non-Gaussian estimation; 5. Biases, correspondences, and outliers; Part II. Three-Dimensional Machinery: 6. Primer on three-dimensional geometry; 7. Matrix lie groups; Part III. Applications: 8. Pose estimation problems; 9. Pose-and-point estimation problems; 10. Continuous-time estimation.

Editorial Reviews

‘This book provides a timely, concise, and well-scoped introduction to state estimation for robotics. It complements existing textbooks by giving a balanced presentation of estimation theoretic and geometric tools and discusses how these tools can be used to solve common estimation problems arising in robotics. It also strikes an excellent balance between theory and motivating examples.’ Luca Carlone, IEEE Control Systems Magazine

From the Publisher