Probabilistic Robotics

by Sebastian Thrun, Wolfram Burgard, Dieter Fox

Publisher: The MIT Press
ISBN: 0262201623

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Book Description

Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations.

This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, http://www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Book Reviews

"*Probabilistic Robotics* is a tour de force, replete with material for students and practitioners alike." - Gaurav S. Sukhatme, Associate Professor of Computer Science and Electrical Engineering, University of Southern CaliforniaPlease note: Arrived too late to appear on book jacket.

About the Author

Sebastian Thrun is Associate Professor in the Computer Science Department at Stanford University and Director of the Stanford AI Lab.

Wolfram Burgard is Professor of Computer Science and Head of the research lab for Autonomous Intelligent Systems at the University of Freiburg.

Dieter Fox is Associate Professor of Computer Science at the University of Washington.

Customer Reviews

This is really an amazing book - it more than fulfilled my expectations. It starts from the very basics of probability theory and clearly derives Kalman Filtering, Particle Filtering, Probabilistic Motion and Probabilistic Perception in the first 6 chapters. From there it moves on to talk about Localization and Mapping completely separately (which I appreciated, since the two topics are far easier to comprehend independently) in chapters 7 and 8 and then finally introduces SLAM (the main topic of the book) in chapter 9. From there it goes on to discuss various SLAM algorithms and implementations, and finally rounds out with planning and control (that is, the practical application of SLAM algorithms).

I can't imagine a more well-researched academic work. Every point is backed up with examples and illustrations, and every algorithm is derived rigorously. Even better, the mathematical derivations are set apart from the main text so that a more "casual" reader can skip over the derivations and still get some benefit from the text (and believe me, the math parts of this book are very involved!). The authors assume a working knowledge of trigonometry, calculus and linear algebra (although you could likely make some sense of the book even if you're rusty in any of these areas). However, since the book is about probability, you'll probably need some background in probability theory to get any value from this text. Chapter 2 contains a refresher on probability theory, but I doubt it would be enough to decipher the later chapters if you had no background in the subject.