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Enhancing FastSLAM with Known Correspondences for Optimal Path Estimation in Robotics

This paper explores a refined implementation of FastSLAM using known correspondences to improve path estimation accuracy. Integrating a particle filter, it estimates the posterior path while employing EKF for feature location estimation. Each particle encompasses the path and an EKF for every feature position, allowing for dynamic updates to both the robot's pose and the features. The methodology includes systematic resampling and new feature initialization, yielding robust performance in various environments. This approach enhances kernel methods and significantly advances state-of-the-art probabilistic robotics.

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Enhancing FastSLAM with Known Correspondences for Optimal Path Estimation in Robotics

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Presentation Transcript

  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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