Particle Filters for Mobile Robot Localization

  • Dieter Fox
  • Sebastian Thrun
  • Wolfram Burgard
  • Frank Dellaert
Chapter
Part of the Statistics for Engineering and Information Science book series (ISS)

Abstract

This chapter investigates the utility of particle filters in the context of mobile robotics. In particular, we report results of applying particle filters to the problem of mobile robot localization, which is the problem of estimating a robot’s pose relative to a map of its environment. The localization problem is a key one in mobile robotics, because it plays a fundamental role in various successful mobile robot systems; see e.g., (Cox and Wilfong 1990, Fukuda, Ito, Oota, Arai, Abe, Tanake and Tanaka 1993, Hinkel and Knieriemen 1988, Leonard, Durrant-Whyte and Cox 1992, Rencken 1993, Simmons, Goodwin, Haigh, Koenig and O’Sullivan 1997, Weiß, Wetzler and von Puttkamer 1994) and various chapters in (Borenstein, Everett and Feng 1996) and (Kortenkamp, Bonasso and Murphy 1998). Occasionally, it has been referred to as “the most fundamental problem to providing a mobile robot with autonomous capabilities” (Cox 1991).

Keywords

Mobile Robot Particle Filter Proposal Distribution Laser Range Finder Global Localization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Dieter Fox
  • Sebastian Thrun
  • Wolfram Burgard
  • Frank Dellaert

There are no affiliations available

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