Probabilistic Robotics (Intelligent Robotics and Autonomous Agents series)
| Author | : | |
| Rating | : | 4.86 (568 Votes) |
| Asin | : | 0262201623 |
| Format Type | : | paperback |
| Number of Pages | : | 672 Pages |
| Publish Date | : | 2013-02-07 |
| Language | : | English |
DESCRIPTION:
Dieter Fox is Associate Professor of Computer Science at the University of Washington.
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. 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 . "Learn the material elsewhere, and then read this book" according to 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 lear. "Delivers even more than it promises" according to 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 derivesKalman Filtering, Particle Filtering, Probabilistic Motion and ProbabilisticPerception in the first 6 chapters. From there it moves on to talk aboutLocalization and Mapping completely separately (which I appreciated, sincethe two topics are far easier to comprehend independently) in chapters 7 a
(Gaurav S. A 'muust have' textbook! (Raja Chatila, LAAS-CNRS, France)Probabilistic Robotics is a tour de force, replete with material for students and practitioners alike. 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. Sukhatme, Associate Professor of Computer Science and Electrical Engineering, University of Southern California)
This book introduces the reader to a wealth of techniques and algorithms in the field. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.. The book's Web site, probabilistic-robotics, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. 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. 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. All algorithms are based on a single overarching mathematical foundation