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Probabilistic Robotics
An introduction to the techniques and algorithms of the newest field in robotics.
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.
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.
672 pages, Kindle Edition
First published September 1, 2005
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Displaying 1 - 8 of 8 reviews
May 24, 2010
I lost my first copy of this book and liked it enough to get another, so I guess I must think it's pretty good.
The main topic is the robotic task of Simultaneous Localization and Mapping (SLAM) and all of the (various) subproblems therein. I find it does a very good job of explaining the basic approaches and their building blocks (e.g. Kalman filters, particle filters) and gives a good foundation for reading more recent work.
The main topic is the robotic task of Simultaneous Localization and Mapping (SLAM) and all of the (various) subproblems therein. I find it does a very good job of explaining the basic approaches and their building blocks (e.g. Kalman filters, particle filters) and gives a good foundation for reading more recent work.
November 7, 2020
Probabilistic Robotics, by Sebastian Thrun, Wolfram Burgard, and Dieter Fox, is a foundational textbook on one of the central problems in autonomous robotics: how a robot can perceive its environment, estimate its own state, and make decisions when its information is incomplete and uncertain. Published by MIT Press in 2005, the 672-page book presents a unified probabilistic framework for dealing with noisy sensors, uncertain motion, imperfect models, and incomplete knowledge. The central idea is both simple and profound: robots cannot be expected to know exactly where they are or what is happening around them. Instead, they must maintain and update beliefs about possible states of the world. The authors develop this idea systematically using probability theory, Bayesian inference, and statistical estimation. The result is a book that connects mathematical theory to important problems in mobile robotics, particularly localization, mapping, simultaneous localization and mapping (SLAM), and decision-making. Although some of its algorithms and implementation practices reflect the era in which it was written, the underlying probabilistic perspective remains highly relevant. Modern robotics continues to require methods for fusing uncertain observations and reasoning about imperfect information; recent surveys of robot localization likewise describe probabilistic state estimation and Bayesian filtering as important foundations for subsequent developments in optimization-based and learning-augmented approaches.
A Unified View of Uncertainty
One of the book's greatest strengths is its attempt to provide a common mathematical language for a wide range of robotics problems. Rather than presenting localization, perception, and mapping as isolated techniques, Thrun, Burgard, and Fox show how they can be understood through probabilistic state estimation. The early chapters establish this foundation through recursive state estimation, Gaussian filters, nonparametric filters, robot motion, and robot perception. The progression is important because it demonstrates how a robot can repeatedly combine what it believed previously with new information from its sensors. Bayes' rule is particularly important to this framework. Instead of treating sensor readings as perfectly accurate measurements, the book represents them probabilistically. A robot therefore does not simply conclude that it is in a particular location; it assigns probabilities to possible locations and updates those probabilities as new evidence becomes available. This approach provides a powerful conceptual framework for understanding autonomous systems. It also explains why uncertainty is not merely a technical nuisance but a fundamental feature of robotics. A robot operating in the real world must deal with sensor noise, ambiguous observations, unpredictable motion, and incomplete maps. Probabilistic reasoning offers a principled way of incorporating these imperfections into the system.
Localization, Mapping, and SLAM
The most compelling sections of the book concern localization and mapping. The authors progressively introduce Markov localization, Gaussian localization, grid-based and Monte Carlo approaches, occupancy-grid mapping, and SLAM. The table of contents illustrates how substantial this treatment is, with separate chapters devoted to occupancy-grid mapping, SLAM, GraphSLAM, the Sparse Extended Information Filter, and FastSLAM. The treatment of SLAM is particularly significant. Simultaneous Localization and Mapping asks a deceptively difficult question: how can a robot construct a map of an unknown environment while simultaneously determining where it is within that map? The two problems are interdependent—the robot needs a reliable position to construct an accurate map, but it also needs a reliable map to determine its position. The probabilistic framework provides a natural way to approach this circular problem. Uncertainty in the robot's position and uncertainty in the map can be represented explicitly and updated as new observations become available. This remains an important area of robotics. More recent work has extended localization and mapping into increasingly sophisticated systems involving visual sensing, inertial measurements, optimization, and machine learning. Nevertheless, the underlying estimation problem remains recognizable, which helps explain why the book continues to appear in discussions and recommendations concerning the theoretical foundations of SLAM. Reader discussions on robotics forums, for example, frequently describe the book as particularly useful for understanding the theory behind SLAM, even when readers recommend newer sources for more advanced or implementation-oriented material.
Particle Filters and Nonparametric Estimation
Another major strength is the treatment of nonparametric estimation and particle filtering. These techniques are especially useful when the probability distributions involved cannot be represented adequately by simple Gaussian models. The particle-filter approach is conceptually powerful because it represents uncertainty through a collection of possible hypotheses, or particles. Instead of forcing uncertainty into a single analytical distribution, the robot can maintain multiple competing possibilities and gradually concentrate them as evidence accumulates. This provides an intuitive bridge between probability theory and practical robotics. A robot that becomes uncertain about its position, for example, can maintain several possible locations rather than committing prematurely to one answer. As additional sensor measurements arrive, unlikely hypotheses can be discarded while more plausible ones receive greater weight. The book's value here is not simply that it introduces particular algorithms. More importantly, it demonstrates how different estimation techniques emerge from the same probabilistic foundations. This unified presentation makes it possible for readers to understand why an algorithm works rather than simply memorizing its procedure.
Mathematical Depth and Pedagogical Approach
The book is mathematically demanding. Its detailed derivations are simultaneously one of its greatest strengths and one of its greatest barriers to entry. MIT Press describes each chapter as containing mathematical derivations, pseudocode implementations, practitioner-oriented discussions, exercises, and class projects. This structure makes the book particularly suitable for university courses and readers who want to understand the mathematics underlying robotics algorithms. For a reader with a strong background in probability, linear algebra, statistics, and programming, the mathematical development can be highly rewarding. Algorithms are not presented as black boxes. Instead, the authors generally explain how they arise from probabilistic assumptions and estimation principles. However, readers without a substantial mathematical background may find the presentation difficult. Probability distributions, conditional probabilities, covariance matrices, recursive estimation, and Bayesian inference appear throughout the text. The derivations can therefore require considerable effort before their practical significance becomes apparent. This is perhaps best understood as a consequence of the book's purpose. Probabilistic Robotics is not primarily a programming manual. It is a textbook designed to explain the theoretical foundations of probabilistic robotics.
A Unified View of Uncertainty
One of the book's greatest strengths is its attempt to provide a common mathematical language for a wide range of robotics problems. Rather than presenting localization, perception, and mapping as isolated techniques, Thrun, Burgard, and Fox show how they can be understood through probabilistic state estimation. The early chapters establish this foundation through recursive state estimation, Gaussian filters, nonparametric filters, robot motion, and robot perception. The progression is important because it demonstrates how a robot can repeatedly combine what it believed previously with new information from its sensors. Bayes' rule is particularly important to this framework. Instead of treating sensor readings as perfectly accurate measurements, the book represents them probabilistically. A robot therefore does not simply conclude that it is in a particular location; it assigns probabilities to possible locations and updates those probabilities as new evidence becomes available. This approach provides a powerful conceptual framework for understanding autonomous systems. It also explains why uncertainty is not merely a technical nuisance but a fundamental feature of robotics. A robot operating in the real world must deal with sensor noise, ambiguous observations, unpredictable motion, and incomplete maps. Probabilistic reasoning offers a principled way of incorporating these imperfections into the system.
Localization, Mapping, and SLAM
The most compelling sections of the book concern localization and mapping. The authors progressively introduce Markov localization, Gaussian localization, grid-based and Monte Carlo approaches, occupancy-grid mapping, and SLAM. The table of contents illustrates how substantial this treatment is, with separate chapters devoted to occupancy-grid mapping, SLAM, GraphSLAM, the Sparse Extended Information Filter, and FastSLAM. The treatment of SLAM is particularly significant. Simultaneous Localization and Mapping asks a deceptively difficult question: how can a robot construct a map of an unknown environment while simultaneously determining where it is within that map? The two problems are interdependent—the robot needs a reliable position to construct an accurate map, but it also needs a reliable map to determine its position. The probabilistic framework provides a natural way to approach this circular problem. Uncertainty in the robot's position and uncertainty in the map can be represented explicitly and updated as new observations become available. This remains an important area of robotics. More recent work has extended localization and mapping into increasingly sophisticated systems involving visual sensing, inertial measurements, optimization, and machine learning. Nevertheless, the underlying estimation problem remains recognizable, which helps explain why the book continues to appear in discussions and recommendations concerning the theoretical foundations of SLAM. Reader discussions on robotics forums, for example, frequently describe the book as particularly useful for understanding the theory behind SLAM, even when readers recommend newer sources for more advanced or implementation-oriented material.
Particle Filters and Nonparametric Estimation
Another major strength is the treatment of nonparametric estimation and particle filtering. These techniques are especially useful when the probability distributions involved cannot be represented adequately by simple Gaussian models. The particle-filter approach is conceptually powerful because it represents uncertainty through a collection of possible hypotheses, or particles. Instead of forcing uncertainty into a single analytical distribution, the robot can maintain multiple competing possibilities and gradually concentrate them as evidence accumulates. This provides an intuitive bridge between probability theory and practical robotics. A robot that becomes uncertain about its position, for example, can maintain several possible locations rather than committing prematurely to one answer. As additional sensor measurements arrive, unlikely hypotheses can be discarded while more plausible ones receive greater weight. The book's value here is not simply that it introduces particular algorithms. More importantly, it demonstrates how different estimation techniques emerge from the same probabilistic foundations. This unified presentation makes it possible for readers to understand why an algorithm works rather than simply memorizing its procedure.
Mathematical Depth and Pedagogical Approach
The book is mathematically demanding. Its detailed derivations are simultaneously one of its greatest strengths and one of its greatest barriers to entry. MIT Press describes each chapter as containing mathematical derivations, pseudocode implementations, practitioner-oriented discussions, exercises, and class projects. This structure makes the book particularly suitable for university courses and readers who want to understand the mathematics underlying robotics algorithms. For a reader with a strong background in probability, linear algebra, statistics, and programming, the mathematical development can be highly rewarding. Algorithms are not presented as black boxes. Instead, the authors generally explain how they arise from probabilistic assumptions and estimation principles. However, readers without a substantial mathematical background may find the presentation difficult. Probability distributions, conditional probabilities, covariance matrices, recursive estimation, and Bayesian inference appear throughout the text. The derivations can therefore require considerable effort before their practical significance becomes apparent. This is perhaps best understood as a consequence of the book's purpose. Probabilistic Robotics is not primarily a programming manual. It is a textbook designed to explain the theoretical foundations of probabilistic robotics.
June 6, 2016
The bible of modern robotics.
January 17, 2023
The best book right now for roboticists and decision making data scientists alike
December 18, 2022
Great book, with clear and full mathematical explanations of complex topics. It is still recommended after 17 years of publishing. Although some topics are a bit outdated, most are fundamental stuff that will outlive the authors.
December 1, 2022
Really quite excellent textbook on a challenging topic. Author does a wonderful job presenting challenging material in a (relatively) easy-to-consume manner.
October 31, 2012
Clear and practical: enthusiastically written.
March 2, 2018
It is a good book for the global planner.
Displaying 1 - 8 of 8 reviews