2. Stanford CS223B Computer Vision, Winter 2005 Lecture 13: Learning Large Environment Models Sebastian Thrun, Stanford Rick Szeliski, Microsoft Hendrik Dahlkamp and Dan Morris, Stanford

3. Sebastian Thrun Stanford University CS223B Computer Vision The SLAM Problem n Simultaneous Localization and Mapping n Same as: Structure from Motion –Large environments –Massive occlusion –Hard correspondence problems Konolige et al, 2001Teller et al, 2000

4. Sebastian Thrun Stanford University CS223B Computer Vision Example

5. Sebastian Thrun Stanford University CS223B Computer Vision Mining Accidents… Somerset County, Quecreek Mine, July, 2002

6. Sebastian Thrun Stanford University CS223B Computer Vision Mining Accidents… Somerset County, Quecreek Mine, July, 2002

7. Sebastian Thrun Stanford University CS223B Computer Vision Mine Subsidence Problems Source: Bureau of Abandoned Mine Reclamation

8. Sebastian Thrun Stanford University CS223B Computer Vision Mine Subsidence Problems Source: Bureau of Abandoned Mine Reclamation

9. Sebastian Thrun Stanford University CS223B Computer Vision Course: CMU RI 16-894 with Red Whittaker, Scott Thayer, 10+ students

10. Sebastian Thrun Stanford University CS223B Computer Vision The Groundhog Robot with Red Whittaker, Scott Thayer, 10+ students

11. Sebastian Thrun Stanford University CS223B Computer Vision Groundhog: Burgesttown, PA

12. Sebastian Thrun Stanford University CS223B Computer Vision Groundhog: Burgesttown, PA

13. Sebastian Thrun Stanford University CS223B Computer Vision Groundhog: Burgesttown, PA

14. Sebastian Thrun Stanford University CS223B Computer Vision 100 Feet In!

15. Sebastian Thrun Stanford University CS223B Computer Vision Operator Control Unit

16. Sebastian Thrun Stanford University CS223B Computer Vision October 27 is Groundhog Day!

17. Sebastian Thrun Stanford University CS223B Computer Vision The Only Mine Map

18. Sebastian Thrun Stanford University CS223B Computer Vision The Basic Problem n Mapping Mines –Very large environments, many cycles –Volumes, centimeter accuracy –Real-time –Autonomous (no communication) n Is instance of: SLAM Problem (Simultaneous Localization and Mapping) –Hundreds of millions of features –Massive data association

19. Sebastian Thrun Stanford University CS223B Computer Vision The Problem: SLAM n Mapping Mines –Very large environments, many cycles –Volumes, centimeter accuracy –Real-time –Autonomous (no communication) n Is instance of: SLAM Problem (Simultaneous Localization and Mapping) –Hundreds of millions of features –Massive data association SLAM with Known Map (Localization) Restriction: Known data association (for now)

20. Sebastian Thrun Stanford University CS223B Computer Vision The Problem: SLAM SLAM with Known Locations (Mapping)SLAM with Known Map (Localization) Restriction: Known data association (for now)

21. Sebastian Thrun Stanford University CS223B Computer Vision The Problem: SLAM SLAM with Known Locations (Mapping)S L A M Restriction: Known data association (for now) Limit

22. Sebastian Thrun Stanford University CS223B Computer Vision EKF Solution [Smith/Cheeseman 1986] S L A M  t covariance m t robot pose and features  t expectation Extended Kalman Filter Restriction: Known data association (for now) Limit

23. Sebastian Thrun Stanford University CS223B Computer Vision EKF Solution [Smith/Cheeseman 1986]  t covariance m t robot pose and features  t expectation Extended Kalman Filter Restriction: Known data association (for now)

24. Sebastian Thrun Stanford University CS223B Computer Vision Classical Solution [Smith/Cheeseman 1986] Extended Kalman Filter

25. Sebastian Thrun Stanford University CS223B Computer Vision Evolution Robotics

26. Sebastian Thrun Stanford University CS223B Computer Vision Maps Acquired by Groundhog 250 meters

27. Sebastian Thrun Stanford University CS223B Computer Vision Maps Acquired by Groundhog Bruceton Research Mine 250 meters

28. Sebastian Thrun Stanford University CS223B Computer Vision Summary SLAM n Is a Hybrid Tracking Problem –Camera pose (robot) –Large number of environmental features –Large number of data association variables n Solution Kalman Filter (very high dimensional)