articleAug 9, 2003Closed access

FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges

Carnegie Mellon University · Stanford University

Abstract

In [15] , Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of FastSLAM that overcomes important deficiencies of the original algorithm. We prove convergence of this new algorithm for linear SLAM problems and provide real-world experimental results that illustrate an order of magnitude improvement in accuracy over the original FastSLAM algorithm.

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878
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Authors

4

Topics & keywords

Keywords
  • Simultaneous localization and mapping
  • Convergence (economics)
  • Algorithm
  • Computer science
  • Particle filter
  • Artificial intelligence
  • Kalman filter
  • Mobile robot
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