Here is the book related to robotics:
Book title: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) Technical details/features and 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 realworld 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 pse
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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.
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Delivers even more than it promises,
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 wellresearched 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/crosscheck (bad). Especially considering the openended
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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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 cuttingedge 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.
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