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Probabilistic Robotics

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ISBN-10: 0262201623

ISBN-13: 9780262201629

Edition: 2005

Authors: Sebastian Thrun, Wolfram Burgard, Dieter Fox

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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…    
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Book details

Copyright year: 2005
Publisher: MIT Press
Publication date: 8/19/2005
Binding: Hardcover
Pages: 672
Size: 8.25" wide x 9.25" long x 1.00" tall
Weight: 3.300
Language: English

Preface
Acknowledgments
Basics
Introduction
Uncertainty in Robotics
Probabilistic Robotics
Implications
Road Map
Teaching Probabilistic Robotics
Bibliographical Remarks
Recursive State Estimation
Introduction
Basic Concepts in Probability
Robot Environment Interaction
Bayes Filters
Representation and Computation
Summary
Bibliographical Remarks
Exercises
Gaussian Filters
Introduction
The Kalman Filter
The Extended Kalman Filter
The Unscented Kalman Filter
The Information Filter
Summary
Bibliographical Remarks
Exercises
Nonparametric Filters
The Histogram Filter
Binary Bayes Filters with Static State
The Particle Filter
Summary
Bibliographical Remarks
Exercises
Robot Motion
Introduction
Preliminaries
Velocity Motion Model
Odometry Motion Model
Motion and Maps
Summary
Bibliographical Remarks
Exercises
Robot Perception
Introduction
Maps
Beam Models of Range Finders
Likelihood Fields for Range Finders
Correlation-Based Measurement Models
Feature-Based Measurement Models
Practical Considerations
Summary
Bibliographical Remarks
Exercises
Localization
Mobile Robot Localization: Markov and Gaussian
A Taxonomy of Localization Problems
Markov Localization
Illustration of Markov Localization
EKF Localization
Estimating Correspondences
Multi-Hypothesis Tracking
UKF Localization
Practical Considerations
Summary
Bibliographical Remarks
Exercises
Mobile Robot Localization: Grid And Monte Carlo
Introduction
Grid Localization
Monte Carlo Localization
Localization in Dynamic Environments
Practical Considerations
Summary
Bibliographical Remarks
Exercises
Mapping
Occupancy Grid Mapping
Introduction
The Occupancy Grid Mapping Algorithm
Learning Inverse Measurement Models
Maximum A Posteriori Occupancy Mapping
Summary
Bibliographical Remarks
Exercises
Simultaneous Localization and Mapping
Introduction
SLAM with Extended Kalman Filters
EKF SLAM with Unknown Correspondences
Summary
Bibliographical Remarks
Exercises
The GraphSLAM Algorithm
Introduction
Intuitive Description
The GraphSLAM Algorithm
Mathematical Derivation of GraphSLAM
Data Association in GraphSLAM
Efficiency Consideration
Empirical Implementation
Alternative Optimization Techniques
Summary
Bibliographical Remarks
Exercises
The Sparse Extended Information Filter
Introduction
Intuitive Description
The SEIF SLAM Algorithm
Mathematical Derivation of the SEIF
Sparsification
Amortized Approximate Map Recovery
How Sparse Should SEIFs Be?
Incremental Data Association
Branch-and-Bound Data Association
Practical Considerations
Multi-Robot SLAM
Summary
Bibliographical Remarks
Exercises
The FastSLAM Algorithm
The Basic Algorithm
Factoring the SLAM Posterior
FastSLAM with Known Data Association
Improving the Proposal Distribution
Unknown Data Association
Map Management
The FastSLAM Algorithms
Efficient Implementation
FastSLAM for Feature-Based Maps
Grid-based FastSLAM
Summary
Bibliographical Remarks
Exercises
Planning and Control
Markov Decision Processes
Motivation
Uncertainty in Action Selection
Value Iteration
Application to Robot Control
Summary
Bibliographical Remarks
Exercises
Partially Observable Markov Decision Processes
Motivation
An Illustrative Example
The Finite World POMDP Algorithm
Mathematical Derivation of POMDPs
Practical Considerations
Summary
Bibliographical Remarks
Exercises
Approximate POMDP Techniques
Motivation
QMDPs
Augmented Markov Decision Processes
Monte Carlo POMDPs
Summary
Bibliographical Remarks
Exercises
Exploration
Introduction
Basic Exploration Algorithms
Active Localization
Exploration for Learning Occupancy Grid Maps
Exploration for SLAM
Summary
Bibliographical Remarks
Exercises
Bibliography
Index