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.
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"*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.
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.
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Customer Reviews
Most Helpful Customer Reviews
24 of 24 people found the following review helpful
By Ravi Mohan
Format:Hardcover
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.
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.
15 of 15 people found the following review helpful
Format:Hardcover
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. I found myself having to
go back and look up the details of Bayes Rule and multivariate conditional
probability more than once.
My only gripe with this book is that each chapter includes suggested exercises
(good) but no answers/cross-check (bad). Especially considering the open-ended
nature of the exercises, it's almost not worth attempting them (or even reading
them), since you'll never know if you got the right answer, or were even on the
right track. There's no "student supplement" (at least not as I write this),
so the exercises are fairly pointless.
However, that aside, this is one of the best academic books I've read in a very
long time. I had been struggling through academic papers from IEEE and ACM on
the topic of SLAM, and only comprehending about half of it before I picked up
"Probabilistic Robotics". After reading this book carefully (I actually had
to read it twice to get it all to sink in), I'm actually zipping through the
academic papers, and understanding everything I read. You couldn't ask for a
better introduction to probabilistic robotics and SLAM.
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. I found myself having to
go back and look up the details of Bayes Rule and multivariate conditional
probability more than once.
My only gripe with this book is that each chapter includes suggested exercises
(good) but no answers/cross-check (bad). Especially considering the open-ended
nature of the exercises, it's almost not worth attempting them (or even reading
them), since you'll never know if you got the right answer, or were even on the
right track. There's no "student supplement" (at least not as I write this),
so the exercises are fairly pointless.
However, that aside, this is one of the best academic books I've read in a very
long time. I had been struggling through academic papers from IEEE and ACM on
the topic of SLAM, and only comprehending about half of it before I picked up
"Probabilistic Robotics". After reading this book carefully (I actually had
to read it twice to get it all to sink in), I'm actually zipping through the
academic papers, and understanding everything I read. You couldn't ask for a
better introduction to probabilistic robotics and SLAM.
11 of 12 people found the following review helpful
By DM
Format:Hardcover
Think of a situation where you had an extremely good lecturer for some subject at uni. The lecturer explained everything very nicely using a host of slides, examples etc. Now, if after the lectures you go back and look at the slides again, you'll probably understand what the slides mean and remember what the lecturer said with regard to each slide. But, as is typical in any course, the slides doesn't contain all the information you learnt during the course. The explanations from the lecturer were critical in understanding the slides.
Now, think of the situation where someone who didn't attend the course, try to learn the subject, just by reading through the slides. This is generally extremely difficult. especially because the slides themselves doesn't contain all the information required to comprehend them.
This book is exactly that. It's a set of slides from a good lecture, but without the important other bits of information required to properly comprehend the ideas presented with it. I feel that the authors assume that the readers already know what they are talking about, and goes on skipping over the details required to properly grasp those concepts. If you've already learnt the topics in the book from by some other means, then you'll be able to understand what the authors are trying to say, and would end up ranking this book as an excellent book. But, my question is, if you already knew those things, why would you read this book in the first place.
BUT, it's not a worthless book. Few of the sections are explained in a very intuitive manner. Also, it's a good book for those who already know the concepts, but want to put them altogether in a coherent manner, with respect to robotics. Also, it is a good source for those who are looking for a list of state-of-the-art algorithms in mobile robotics.
Now, think of the situation where someone who didn't attend the course, try to learn the subject, just by reading through the slides. This is generally extremely difficult. especially because the slides themselves doesn't contain all the information required to comprehend them.
This book is exactly that. It's a set of slides from a good lecture, but without the important other bits of information required to properly comprehend the ideas presented with it. I feel that the authors assume that the readers already know what they are talking about, and goes on skipping over the details required to properly grasp those concepts. If you've already learnt the topics in the book from by some other means, then you'll be able to understand what the authors are trying to say, and would end up ranking this book as an excellent book. But, my question is, if you already knew those things, why would you read this book in the first place.
BUT, it's not a worthless book. Few of the sections are explained in a very intuitive manner. Also, it's a good book for those who already know the concepts, but want to put them altogether in a coherent manner, with respect to robotics. Also, it is a good source for those who are looking for a list of state-of-the-art algorithms in mobile robotics.
Most Recent Customer Reviews
5.0 out of 5 stars
Excellent material, excellent exposition
Sebastian Thrun has a unique talent for exposition of very complicated material. The book covers the probability and statistics needed to understand the material, and does not... Read more
Published 9 months ago by Stephen
5.0 out of 5 stars
Outstanding book covering the latest in the field
Dense for those unfamiliar with complex mathematics. A thorough and broad treatment of state-of-the-art techniques and algorithms for robotics. A must-read. Read more
Published 12 months ago by Tennessee Leeuwenburg
5.0 out of 5 stars
Excellent content.. prevalent errors are a (minor) problem
Overall, this is an EXCELLENT book. It is packed with really good information and explained in well written English. Read more
Published 12 months ago by David
4.0 out of 5 stars
Overall good
The material of this book is overall very good.
The detractor is that the pseudocode is very poor. Read more
The detractor is that the pseudocode is very poor. Read more
Published 14 months ago by Aaron
5.0 out of 5 stars
Pleased with first 3 chapters
I have only made it into the first 3 chapters. So far, I am enjoying the text. The second chapter was an excellent review of the basic concepts of Bayesian statistics.
Published 15 months ago by Jeremy
5.0 out of 5 stars
Excellent book!
This is an excellent text for those who want to learn about probabilistic robotics. It starts with the very basics and presents lots of interesting algorithms.
Published 15 months ago by Felipe N. Martins
3.0 out of 5 stars
Could have been much better
The book covers many 'hot' topics in robotics research including SLAM methods as well as more proven techniques like Monte Carlo localization. Read more
Published on January 7, 2011 by Brian
5.0 out of 5 stars
Great Reference Book!
Just went through this book in a week for my own research. This was a nice read and a great reference book for all the probabilistic theory and algorithms that are essential to... Read more
Published on December 26, 2010 by Mike
4.0 out of 5 stars
The Robotics Reference
This textbook is the standard reference for probabilistic robotics in the areas of navigation and mapping. Read more
Published on July 25, 2010 by Chris Mansley
3.0 out of 5 stars
plenty of math and theory ....
This book has plenty of math and theory in regards to state estimation and SLAM. However, it is really lacking in details and examples of implementation. Read more
Published on June 13, 2009 by K., Marc
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