FastSLAM is a Rao-Blackwellized particle filter for simultaneous localization and mapping. The pose of the robot in the environment is represented by a particle filter. Furthermore, each particle carries a map of the environment, which it uses for localization. In the case of landmark-based FastSLAM, the map is represented by a Kalman Filter, estimating the mean position and covariance of landmarks.
landmark-based FastSLAM algorithm:
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data: This folder contains files representing the world definition and sensor readings used by the filter.
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starter code: This folder contains the FastSLAM starter code.
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doc This folder contains the detailed listing of the algorithm as a PDF file.
Done using Dijkstra and A* algorithms