| View Larger Image | Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) | Hardcoverby Sebastian Thrun (Author), Wolfram Burgard (Author), Dieter Fox (Author)
| List Price: | $58.00 | | Price: | $37.42 | | You Save: | $20.58 (35%) | | | Available: | Usually ships in 24 hours |
| | Binding: | Hardcover | | Publisher: | The MIT Press | | Page Count: | 667 Pages | | Publication Date: | September 01, 2005 | | Sales Rank: | 137,316th |
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EDITORIAL REVIEWS |
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. |
CUSTOMER REVIEWS (Average Customer Rating: 4.5 based on 10 reviews)
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plenty of math and theory .... by K., Marc  3 Stars
June 13, 2009
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. Many of the problems at the end of the chapters rely on material from the next chapters. Most of what I learned about Kalman and Extended Kalman filters in the past made more sense as I read this book. However, new material such as particle filters was difficult to understand from the text alone. I had to look elsewhere for examples of implementation/tutorials.
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One of the best books that I have ever read. by Thiago de Freitas Oliveira Araujo  5 Stars
February 14, 2009
This book is essential for anyone that works or make some research on Robotics, especially on Slam.
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Delivers even more than it promises by Joshua Davies (Dallas, TX United States)  4 Stars
August 28, 2008
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.
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Great Book! by felipegermany 5 Stars
April 05, 2008
I consider this book the most valuable resource in the field! If you are really interested in implementing kalman filter localization, particle filter localization or SLAM algorithms, this book really will help you. This book was my reference during my Master Thesis and the algorithms are so comprenhensive that I hadn't any problem to put them running.
I think the autors made a really good effort to explain complex mathematical concepts as clearly as possible. Great Job!
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Excellent resource for implementing SLAM by Billy McCafferty (Denver, CO United States) 5 Stars
September 18, 2007
This is by far the best resource that I have found for collating a large number of internally consistent SLAM algorithms into a single volume. The book carefully leads the reader through the requirements of SLAM presenting one algorithm at a time, building upon the algorithms presented previously. This approach lends itself very well to develop-while-you-read. If you care to do so, I recommend reading it through once in its entirety and then starting over for the develop-while-you-read approach. The once through does a good job of presenting the big picture and giving you the opportunity to decide which primary SLAM path you prefer; Kalman and particle filtering are the two main approaches discussed. I'm currently implementing FastSLAM with particle filtering and have not run into any large hurdles using this book to lead the way.
The only major challenge that I've encountered is that it assumes a very good understanding of probability distributions. A good college statistics book makes a good companion for this read.
I also read Thrun's FastSLAM monograph. There's very little new information in that monograph which Probabilisitc Robotics doesn't already cover. After reading PR, Google becomes your best resource for finding the latest algorithms and code samples. Because even with the descriptive pseudo code algorithms, a perfect follow-up to this book would be "Probabilistic Robotics Implemented" with lots of code samples.
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