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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, 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.
- Sales Rank: #639719 in eBooks
- Published on: 2005-08-19
- Released on: 2005-08-19
- Format: Kindle eBook
Review
A robot is an uncertainty machine: its perception and decision-making capabilities must embed at their core the processes dealing with uncertainty. The book is an essential reference for the student, the teacher, and the researcher to understand the basics and the advanced methods of estimation theory, and the probabilistic models and processes underlying robot localization, SLAM, and decion making. A 'muust have' textbook!
(Raja Chatila, LAAS-CNRS, France)
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 California)
About the Author
Dieter Fox is Associate Professor of Computer Science at the University of Washington.
Most helpful customer reviews
37 of 37 people found the following review helpful.
Superb
By Ravi Mohan
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.
25 of 25 people found the following review helpful.
Delivers even more than it promises
By Joshua Davies
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.
23 of 27 people found the following review helpful.
Learn the material elsewhere, and then read this book
By DM
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.
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