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.

Table of Contents

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

Ill 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

Review

A robot is an uncertainty machine: its perception and decision-making capabilities must embed at their core the processes dealing with uncertainty. The book is an essential reference for the student, the teacher, and the researcher to understand the basics and the advanced methods of estimation theory, and the probabilistic models and processes underlying robot localization, SLAM, and decion making. A 'muust have' textbook!

―Raja Chatila, LAAS-CNRS, France

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 California

About the Authors

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.