Product 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.

Product Details

• Amazon Sales Rank: #254902 in Books
• Published on: 2005-09-01
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 667 pages

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

Superb
The authors took 6 years to write this book. And it shows. This is a mindblowing tour through the algorithms used at the cutting edge of Robotics.

What is good

1. Every algorithm has descriptive text, mathematical derivations AND pseudo code. More importantly it all meshes into a cohesive whole.

2. The progression of chapters is excellent, starting with basic algorithms and proceeding to more advanced/refined algorithms.

3.There is a consistent practical focus with algorithms being explained in the context of solving real world problems in robotics.

4. The exercises are few in number , but are *perfect* to illuminate each chapter's ideas and encourage the reader to start thinking on his own.

5. There is a comprehensive errata page on the book's website.

6. Last but not least, the tone of the writing is very engaging. The reader is not talked down to. It is almost as if the authors were in your study carefully guiding you through an intellectual wonderland.

The bad.
Hmmm i can't think of anything. It is great book. I just wish the authors would write MORE books like this :-)



About the only caveat is that a reader should have *some* degree of mathematical insight before attempting this book. The authors do cover elementary probability theory etc in the initial chapters, and they do a good job given the space constraints. But in my opinion if you have absolutely no experience in probability theory or calculus, you should probably learn from other books and then tackle this one. This is, after all, a graduate level text.

an impressive research-level text
The book presents what is currently the frontier of probabilistic research in robotics. This is explained as a means of a robot coping with inadequate information from its perceptive inputs. The intent is to embed more robust control logic within the robot. Rather than having human programmers try to code for every contingency.

There are many algorithms in the text. Each is explicitly defined in pseudocode. But just as significantly, each is accompanied by extensive textual explanations and derivations. These are rounded out by the chapters having exercises that extend the ideas developed in each chapter.

Many ideas from statistics are applied here, from Markov processes to Monte Carlo samplings to Bayesian inferences.

A superb textbook and reference
Robotics is a vast field. At the center of its computational side are the algorithms for spatial reasoning: mapping, localizing, and navigating. This book covers these fundamentals more thoroughly and comprehensively than any other. The algorithms described here are already the de facto starting point for cutting-edge work in computational robotics. Variants on these ideas make up a huge part of ongoing research, as evidenced by current journal articles and conference papers.

This is not an introductory text. There are many excellent choices for that kind of broad coverage of robotics, even computational robotics. Rather, this book is to robotics what Vision: A Modern Approach is to the field of computer vision. Even at the speed the field is moving, this book will be a standard for many years.