State Estimation for Robotics
Second Edition

This modern look at state estimation now covers variational inference, adaptive covariance estimation, and inertial navigation.

Timothy D. Barfoot (Author)

9781009299893, Cambridge University Press

Hardback, published 1 February 2024

530 pages
26 x 18.5 x 3.5 cm, 1.174 kg

'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

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.

Acronyms and abbreviations
Notation
Foreword to first edition
Foreword to second edition
1. Introduction
Part I. Estimation Machinery: 2. Primer on probability theory
3. Linear-Gaussian estimation
4. Nonlinear non-Gaussian estimation
5. Handling nonidealities in estimation
6. Variational inference
Part II. Three-Dimensional Machinery: 7. Primer on three-dimensional geometry
8. Matrix lie groups
Part III. Applications: 9. Pose estimation problems
10. Pose-and-point estimation problems
11. Continuous-time estimation
Appendix A: matrix primer
Appendix B: rotation and pose extras
Appendix C: miscellaneous extras
Appendix D: solutions to exercises
References
Index.

Subject Areas: Image processing [UYT]