Probabilistic Robotics (2005)正式版本.pdf

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Contents

Preface

Acknowledgments

I Basics

1 Introduction

2 Recursive State Estimation

3 Gaussian Filters

4 Nonparametric Filters

5 Robot Motion

6 Robot Perception

II Localization

7 Mobile Robot Localization: Markov and Gaussian

8 Mobile Robot Localization: Grid And Monte Carlo

III Mapping

9 Occupancy Grid Mapping

10 Simultaneous Localization and Mapping

11 The GraphSLAM Algorithm

12 The Sparse Extended Information Filter

13 The FastSLAM Algorithm

IV Planning and Control

14 Markov Decision Processes

15 Partially Observable Markov Decision Processes

16 Approximate POMDP Techniques

17 Exploration

Bibliography

Index

Probabilistic Robotics

Probabilistic Robotics Sebastian Thrun Wolfram Burgard Dieter Fox The MIT Press Cambridge, Massachusetts London, England

© 2006 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. MIT Press books may be purchased at special quantity discounts for business or sales promotional use. For information, please email special_sales@mitpress.mit.edu or write to Special Sales Department, The MIT Press, 55 Hayward Street, Cambridge, MA 02142. Typeset in 10/13 Lucida Bright by the authors using LATEX 2ε. Printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data Thrun, Sebastian, 1967– Probabilistic robotics / Sebastian Thrun, Wolfram Burgard, Dieter Fox. p. cm. – (Intelligent robotics and autonomous agents series) Includes bibliographical references and index. ISBN-13: 978-0-262-20162-9 (alk. paper) 1. Robotics. 2. Probabilities. I. Burgard, Wolfram. II. Fox, Dieter. III. Title. IV. In- telligent robotics and autonomous agents. TJ211.T575 2005 629.8’92–dc22 2005043346 10 9 8 7 6 5 6 3

Brief Contents 1 3 Introduction Recursive State Estimation Gaussian Filters I Basics 1 2 3 4 Nonparametric Filters 117 5 6 Robot Motion Robot Perception 149 39 13 85 II Localization 7 Mobile Robot Localization: Markov and Gaussian 8 Mobile Robot Localization: Grid And Monte Carlo 189 191 237 279 III Mapping 9 Occupancy Grid Mapping 10 Simultaneous Localization and Mapping 11 The GraphSLAM Algorithm 12 The Sparse Extended Information Filter 13 The FastSLAM Algorithm 281 337 437 309 385 IV Planning and Control 14 Markov Decision Processes 15 Partially Observable Markov Decision Processes 485 487 513

vi Brief Contents 16 Approximate POMDP Techniques 17 Exploration 569 547

Contents Preface xvii Acknowledgments xix I Basics 1 1 Introduction 3 3 1.1 Uncertainty in Robotics 1.2 1.3 1.4 1.5 1.6 Probabilistic Robotics Implications Road Map Teaching Probabilistic Robotics Bibliographical Remarks 11 9 10 4 11 2 Recursive State Estimation 13 2.1 2.2 2.3 2.4 13 26 28 14 19 20 State Environment Interaction Probabilistic Generative Laws Belief Distributions Introduction Basic Concepts in Probability Robot Environment Interaction 2.3.1 2.3.2 2.3.3 2.3.4 Bayes Filters 2.4.1 2.4.2 2.4.3 Mathematical Derivation of the Bayes Filter 2.4.4 The Bayes Filter Algorithm Example The Markov Assumption 25 22 24 26 33 31

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