Probabilistic Robotics / Edition 1

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Overview

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 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.

The MIT Press

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What People Are Saying

From the Publisher

"*Probabilistic Robotics* is a tour de force, replete with material for students and practitioners alike."--Gaurav S. Sukhatme, Associate Professor of Computer Science and Electrical
Engineering, University of Southern CaliforniaPlease note: Arrived too late to appear on book jacket.

The MIT Press

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Product Details

  • ISBN-13: 9780262201629
  • Publisher: MIT Press
  • Publication date: 9/1/2005
  • Series: Intelligent Robotics and Autonomous Agents series
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 672
  • Sales rank: 569,138
  • Product dimensions: 8.00 (w) x 9.00 (h) x 1.12 (d)

Meet the Author

Sebastian Thrun is Associate Professor in the Computer Science Department at Stanford
University and Director of the Stanford AI Lab.

Wolfram Burgard is Professor of Computer Science and Head of the research lab for Autonomous
Intelligent Systems at the University of Freiburg.

Dieter Fox is Associate Professor of Computer Science at the University of Washington.

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Table of Contents

1 Introduction 3
2 Recursive state estimation 13
3 Gaussian filters 39
4 Nonparametric filters 85
5 Robot motion 117
6 Robot perception 149
7 Mobile robot localization : Markov and Gaussian 191
8 Mobile robot localization : grid and Monte Carlo 237
9 Occupancy grid mapping 281
10 Simultaneous localization and mapping 309
11 The GraphSLAM algorithm 337
12 The sparse extended information filter 385
13 The FastSLAM algorithm 437
14 Markov decision processes 487
15 Partially observable Markov decision processes 513
16 Approximate POMDP techniques 547
17 Exploration 569
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