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SLAM Summer School 2004

A note to students. The lecture I give will not include all these slides. Some of then and some of the notes I have supplied are more detailed than required and would take too long to deliver. I have included them for completeness and background for example the derivation of the Kalman filter fro

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SLAM Summer School 2004

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    1. SLAM Summer School 2004 An Introduction to SLAM – Using an EKF Paul Newman Oxford University Robotics Research Group

    2. A note to students The lecture I give will not include all these slides. Some of then and some of the notes I have supplied are more detailed than required and would take too long to deliver. I have included them for completeness and background – for example the derivation of the Kalman filter from Bayes Rule. I have included in the package a working matlab implementation of EKF based SLAM. You should be able to see all the properties of SLAM at work and be able to modify at your leisure. (without having to worry about the awkwardness of a real system to start with). I cannot cover all I would like to in the time available – where applicable, to fill gaps, I forward reference other talks that will be given during the week. I hope the talk, the slides and the notes will whet you appetite regarding what I reckon is great area of research. Above all, please please ask me to explain stuff that is unclear – this school is about you learning, not us lecturing. regards Paul Newman

    3. Overview Kalman Filter was the first tool employed in SLAM – Smith Self and Cheeseman. Linear KFs implement Bayes rule. No hokie-ness We can analyse KF properties easily and learn interesting things about Bayesian SLAM The vanilla, monolithic, KF-SLAM formulation is a fine tool for small local areas But we can do better for large areas – as other speakers will mention

    4. 5 Minutes on Estimation

    5. Estimation is …..

    6. Minimum Mean Squared Error Estimation

    7. Evaluating….

    8. Recursive Bayesian Estimation

    9. Yes…

    11. Kalman Filtering

    12. Overall Goal

    13. Covariance is…..

    14. The i|j notation

    15. The Basics

    17. Crucial Characteristics

    18. Nonlinear Kalman Filtering

    20. Using The EKF in Navigation

    21. Vehicle Models - Prediction

    22. Noise is in control….

    23. Effect of control noise on uncertainty:

    24. Using Dead-Reckoned Data

    25. Navigation Architecture

    26. Background T-Composition

    27. Just functions!

    28. Deduce an Incremental Move

    29. Use this “move” as a control

    30. Feature Based Mapping and Navigation

    31. Mapping vs Localisation

    32. Problem Space

    33. Problem Geometry

    34. Landmarks / Features

    35. Observations / Measurements

    36. And once again…

    37. From Bayes Rule…..

    38. Problem 1 - Localisation

    39. We can use a KF for this!

    40. Processing Data

    41. Implementation

    42. Location Covariance

    43. Location Innovation

    44. Problem II Mapping

    45. But how is map built?

    46. How is P augmented?

    47. Leading to :

    48. So what are models h and f?

    49. Turn the handle on the EKF:

    50. Problem III SLAM

    52. How Is that sum evaluated? A current area of interest/debate Monte-carlo Methods Thin Junction Trees Grid based techniques Kalman Filter

    53. Naďve SLAM

    54. Prediction:

    55. Feature Initialisation:

    56. EKF SLAM DEMO

    58. Laser Sensing

    59. Extruded Museum

    60. SLAM in action

    61. Human Driven Exploration

    66. The Convergence and Stability of SLAM

    67. We can show that:

    76. Proofs Condensed (9)

    77. Take home points:

    78. Issues:

    79. Data Association – a big problem

    80. The Problem with Single Frame EKF SLAM

    82. Closing thoughts

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