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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4Topics & keywords
Topics
Keywords
- Simultaneous localization and mapping
- Convergence (economics)
- Algorithm
- Computer science
- Particle filter
- Artificial intelligence
- Kalman filter
- Mobile robot
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