Robot Compagnion Localization at home and in the office

1. Robot Compagnion Localization at home and in the office Arnoud Visser, Jürgen Sturm, Frans Groen University of Amsterdam Informatics Institute

2. Overview • Mobile robotics • Robot localization • Presentation of the panorama approach • Results • Demonstration videos

3. Mobile robotics Robot cranes and trucks unloading ships Port of Rotterdam Sony Aibos playing soccer Cinekids, De Balie, Amsterdam SICO at Kosair Children's Hospital Dometic, Louisville, Kentucky RC3000, the robocleaner Kärcher

4. The localization problem • Robot localization .. is the problem of estimating the robot’s pose relative to a map of the environment. • Position tracking • Global localization • Kidnapping problem

5. Localization • Sensors • Odometry, GPS, Laserscanner, Camera.. • Feature space • World representation • Topological graphs, grid-based maps • Filters • Kalman filters, particle filters

6. Classical approaches • Special environments • (Visual) landmarks • (Electro-magnetic) guiding lines • Special sensors • GPS • Laser-scanners • Omni-directional cameras • Special requirements • Computationally heavy (offline computation)

7. New approach • Natural environments • Human environments • Unstructured and/or unknown for the robot • Normal sensors • Camera • Reasonable requirements • Real-time • Moderate hardware requirements

8. Platform: Sony Aibo • Computer • 64bit RISC processor • 567 MHz • 64 MB RAM • 16 MB memorystick • WLAN • Internal camera • 30fps • 208x160 pixels • Actuators • Legs: 4 x 3 joints • Head: 3 joints

9. Demo: Compass Library, University of Amsterdam

10. Synopsis

11. Color segmentation Raw image Sidetrack: Color Calibration • Robot collects colors from environment • Colors are clustered using an EM algorithm • Color-to-Colorclass lookup table is created for faster access Color class image

12. Mathematics rotation translation feature vector ideal world model learned world model

13. Feature space conversion

14. Feature vectors and world model Feature vector consists of color transition counts between the n color classes World model distribution

15. Feature space conversion (2) Raw image Color class image Sector-based feature vectors

16. Learning Update distribution of single color class transition by updating the constituting counters

17. Matching Likelihood of Single sector Adjacent sectors Rotation estimate Confidence estimate

18. Post-processing: Compass Idea: smooth rotational estimate over multiple frames + removes outliers + stabilizes estimate + integrates (rotational) odometry

19. Results: Compass Brightly illuminated living room

20. Results: Compass Daylight office environment

21. Results: Compass Outdoor soccer field

22. Results: Compass Robocup 4-Legged soccer field

23. Signal degradation (w.r.t. distance) Robocup 4-Legged soccer field

24. Post-processing: Grid localization Idea: learn multiple spots, then use confidence value to estimate the robot‘s position in between – fixed grid (better: self-learned graph based on confidence) – difficult to integrate odometry + proof of concept

25. Demo: Grid localization Robocup 4-Legged soccer field

26. Results: Grid localization Robot walks back to center after kidnap 100 75 50 25 Positioning accuracy y [cm] 0 -100 -75 -50 -25 0 25 50 75 100 -25 -50 -75 -100 x [cm] Robocup 4-Legged soccer field

27. Conclusions • Novel approach to localization: • Works in unstructured environments • Tested on various locations • Interesting approach for mobile robots at home and in the office

28. Questions?