Probabilistic Robotics

Name: Probabilistic Robotics
Price: 71.32 USD
Availability: InStock
ISBN: 9780262201629

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…

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: 3.234
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