PDF Ebook Probabilistic Robotics (Intelligent Robotics and Autonomous Agents series)
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Probabilistic Robotics (Intelligent Robotics and Autonomous Agents series)
PDF Ebook Probabilistic Robotics (Intelligent Robotics and Autonomous Agents series)
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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, FranceProbabilistic 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
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About the Author
Sebastian Thrun is Associate Professor in the Computer Science Department at Stanford University and Director of the Stanford AI Lab.
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Product details
Series: Intelligent Robotics and Autonomous Agents series
Hardcover: 672 pages
Publisher: The MIT Press; 1 edition (August 19, 2005)
Language: English
ISBN-10: 0262201623
ISBN-13: 978-0262201629
Product Dimensions:
8 x 1.4 x 9 inches
Shipping Weight: 3 pounds (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
49 customer reviews
Amazon Best Sellers Rank:
#57,036 in Books (See Top 100 in Books)
I work in avionics, not robotics, but I ordered this book because it seemed to cover a lot of the subjects that are now making their way into avionics systems. In particular, I was interested in its coverage of Kalman Filters and POMDPs.I have to say that the other positive reviews are well-warranted. I have not before encountered such clear explanations of Bayes filtering, Kalman Filters (including EKFs and UKFs), even in spite of having encountered many books and papers on these subjects. The authors seem to go out of their way to present the material with a logical and clear-cut progression that doesn't skip essential steps with the typical "the reader can clearly see that..." kinds of hand-waiving I have seen in other texts.To be honest, I can't comment on the parts related to robot dynamics and SLAM, but as for chapters 1-4 and the chapters on POMDPs, I would have to say that this book presents the material in a better and more clear way than I have ever seen it presented before.
This is my go-to book for reference and to revisit fundamentals. However some newer techniques have emerged which are worth covering in a next edition which is sorely needed.
This textbook is the standard reference for probabilistic robotics in the areas of navigation and mapping. One of the authors is the director of the Stanford AI lab and headed the winning entry in the DARPA Grand Challenge in 2007, which needless to say means he understands and has developed many of the techniques in the book. The algorithms are laid out and explained at different depths of understanding, which sometimes allows them to be used without reading the rigorous mathematical derivations that are included. Within the first week of having this book, I found that my method of estimating odometry in the prediction step of a Kalman filter could be improved with a different estimation. In addition, since the book provided a mathematical derivation, I could compare the two techniques and explain under what assumptions my approximation fails to do well.
The books has a very practical approach to the issues of robotics. I wish economist could use similar probabilistic models to make their predictions/forecasts more realistic.
This is a brilliant book, must read and must have on a desk of a robotics engineer, especially on the software side.One can tell how much effort and hard work the authors put into writing the book.Very clean and concise text.The book not just teaches the subject, but gives a lot of ideas on further research topics.It might sound like the authors have been primarily concerned with SLAM and motion planning problems. The truth is that the methods they describe can be applied in many other areas, just use your brain.Wish there were more books like that.
Bought this for my son, who understands it.
I like the book a lot and fits perfect in what I am working! Thank you...
As someone with a multi-discipline background that includes some control theory, I am always frustrated by the "explanations" that control theorists attempt to put forward for the Kalman filter, and find that the best explanations actually come from other fields. Prior to stumbling upon this book, the best explanation of the Kalman filter I had read actually came from a book on Bayesian statistics. It makes sense that this book would have the same basic Bayesian approach, but would also extend the technique in exactly the manner needed to properly do controls and robotics. Further it fits the technique into a larger, cross-disciplinary, landscape of estimation, sensing and modeling techniques from both "controls" and "robotics" (which, despite sounding like two forms of the same field, tend to actually have distinct and disparate communities which have surprising trouble talking to one another).
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