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Landmark based FastSLAM implementation followed by global path planning for a small four wheeled robot

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Fast-SLAM-Global-Path-Planning

Fast SLAM Implementation

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:

  1. data: This folder contains files representing the world definition and sensor readings used by the filter.

  2. starter code: This folder contains the FastSLAM starter code.

  3. doc This folder contains the detailed listing of the algorithm as a PDF file.

Global Path Planning

Done using Dijkstra and A* algorithms

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Landmark based FastSLAM implementation followed by global path planning for a small four wheeled robot

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