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Probabilistic Robotics

Probabilistic Robotics. FastSLAM with known correspondences. Path posterior estimated by a particle filter Feature locations estimated by EKFs. M particles M*N EKFs. Each particle contains: Path estimation EKF for each feature location. Pose estimate 1-4 Update features 5-24

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Probabilistic Robotics

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  1. Probabilistic Robotics FastSLAM with known correspondences Richard Mauldin

  2. Path posterior estimated by a particle filter • Feature locations estimated by EKFs

  3. M particles • M*N EKFs

  4. Each particle contains: • Path estimation • EKF for each feature location

  5. Pose estimate • 1-4 • Update features • 5-24 • Resampling • 25-30

  6. Update path with pose estimate

  7. 3.Retrieves information for particle k • 4.Samples new robot pose based on control • Pose added to particle k’s path estimation

  8. Update features

  9. Determine if the observed feature is new • 7.Initialize mean using measurement function h

  10. Resampling

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