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. Nave 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
