Table of Contents

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.

Promotional Information

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

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.

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