State Estimation for Robotics (2nd Edition)

By Timothy D. Barfoot

State Estimation for Robotics, Second Edition, is a leading graduate-level textbook that presents a modern and unified treatment of probabilistic state estimation methods used in robotics. Published by Cambridge University Press, this comprehensive volume integrates theory, mathematics, and practical robotics applications.

The 2nd Edition expands coverage of Lie groups, nonlinear estimation, factor graphs, and optimization-based methods. It provides a rigorous yet accessible foundation for Kalman filtering, extended and unscented filters, SLAM (Simultaneous Localization and Mapping), and motion estimation in robotic systems.

What This Book Does

This book equips robotics engineers and graduate students with the mathematical tools and probabilistic frameworks necessary to estimate the state of dynamic systems under uncertainty.

Key Features of the 2nd Edition

• Comprehensive treatment of Bayesian estimation
• Detailed coverage of Kalman and nonlinear filters
• Expanded discussion of Lie groups and manifold theory
• Factor graphs and optimization-based state estimation
• Applications to SLAM and robotic motion estimation

Who Should Use This Book?

• Graduate students in robotics and control systems
• Robotics engineers and researchers
• Autonomous systems developers
• Computer vision and AI practitioners
• Faculty teaching robotics and estimation theory

Why It’s Essential

• Widely respected robotics estimation reference
• Balances rigorous mathematics with practical insight
• Supports advanced research in autonomous systems
• Trusted textbook in robotics graduate programs

A definitive guide to probabilistic state estimation in robotics.
Order today from BooksGoat and advance your robotics expertise.

Product Details

• ISBN-13: 9781009299893
• Edition: 2nd
• Author: Timothy D. Barfoot
• Publisher: Cambridge University Press
• Format: Hardcover
• Condition: New
• Availability: In Stock
• Price: $— (Free Shipping)

Table of Contents (Highlights)

• Probability and Bayesian Estimation
• Linear and Nonlinear Kalman Filtering
• State Representation on Manifolds
• Factor Graphs and Optimization Methods
• SLAM and Localization
• Applications in Robotics and Autonomous Systems

FAQs

• Is this suitable for graduate study?
  Yes. It is widely used in robotics and control systems graduate programs.
• Does it cover SLAM?
  Yes. It includes detailed coverage of SLAM and related estimation methods.
• Is it mathematically rigorous?
  Yes. The text provides a strong theoretical foundation.