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.

• Basics
  • Introduction
  • Recursive State Estimation
  • Gaussian Filters
  • Nonparametric Filters
  • Robot Motion
  • Robot Perception
• Localization
  • Mobile Robot Localization: Markov and Gaussian
  • Mobile Robot Localization: Grid And Monte Carlo
• Mapping
  • Occupancy Grid Mapping
  • Simultaneous Localization and Mapping
  • The GraphSLAM Algorithm
  • The Sparse Extended Information Filter
  • The FastSLAM Algorithm
• Planning and Control
  • Markov Decision Process
  • Partially Observable Markov Decision Processes
  • Approximate POMDP Techniques
  • Exploration
  • Bibliography
  • Index