1. Introduction to Probabilistic Robot Mapping

2. What is Robot Mapping? General Definitions for robot mapping

3. Terms and concepts related to Robot Mapping

4. What is SLAM?

5. Example of Localization for a mobile robot Yellow means fixed firm information Predicted state Robot knows map Robot knows landmarks on map Robot sees landmarks Robot wants to estimate its pose

6. Example of Mapping estimate given Robot does not know the map or its part Robot knows its pose Robot sees landmarks Robot wants to estimate landmarks on the map to create or update or extend the map. Robot creates the map

7. Real value Predicted value Robot does not know the map or its part Robot estimates its pose Robot sees landmarks Robot wants to estimate landmarks on the map to create or update or extend the map. Example of SLAM

8. The SLAM problem is chicken-or-egg problem

9. SLAM Problem is very important SLAM is the fundamental problem in robot navigation. You cannot avoid it.

10. Applications of SLAM In MCECSBOT we do not have SLAM as the map is known. SLAM can be used for furniture only and items that are not on a map of the building

14. Formal Definition of the SLAM Problem

15. Definition of the SLAM Problem

16. All our work is based on Probabilistic Approaches

17. Representation of robot’s uncertainty in probabilistic terms We use the same notation as in past lectures

18. Graphical Model of Full SLAM path observations map controls

19. Full SLAM versus Online SLAM

20. Graphical Model of Online SLAM FULL SLAM Let us compare full SLAM and Online SLAM

21. Online SLAM

22. Graphical Model of Online SLAM to explain the integrations

23. Why SLAM problem is so hard to solve? The problem can be solved because map and pose estimates are correlated

24. Why SLAM is a hard problem to solve? More reasons why it is so hard.

25. Taxonomy of SLAM problems

31. In active SLAM we have a feedback to make decision where to go next

33. Time is restrictedSpace is restricted

34. Approaches to solve the SLAM problem

36. Main Paradigms for SLAM

37. Models for SLAM

38. Model of Motion and Observation

39. Model of Motion for SLAM

40. Examples of Models of Motion

41. STANDARD ODOMETRY Model for motion of a robot new data old controls Calculate new data from old data and controls

43. Model of Observation of Sensor

44. Examples of Observation Model

46. Summary on SLAM