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Recursive State Estimation Lecture 7 Perception as a Continuous - PowerPoint PPT Presentation

Recursive State Estimation Lecture 7 Perception as a Continuous Process Perception as a Multi-Modal Experience Perception as Inference Helmholtz (1821-1894) Plato (I BC) Recursive State Estimation Mathematical Formalism to: continuously


  1. Recursive State Estimation Lecture 7

  2. Perception as a Continuous Process

  3. Perception as a Multi-Modal Experience

  4. Perception as Inference Helmholtz (1821-1894) Plato (I BC)

  5. Recursive State Estimation Mathematical Formalism to: continuously integrate measurements ○ from different sensor sources ○ to infer the state of a latent variable ○

  6. Why is this Useful for Robotics?

  7. Today Intro: why state estimation? ● Bayes Filter ● Kalman Filter ●

  8. The Agent and the Environment Decision Making World Model Perception

  9. Notation

  10. Probability Theory Refresh

  11. Probability Theory Refresh 2

  12. Probability Theory Refresh 3

  13. The Bayes Filter

  14. The Bayes Filter

  15. Limitations

  16. Kalman Filter

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