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Panoramic

Panoramic. University of Amsterdam Informatics Institute. Localization. with a Sony Aibo. by Jürgen Sturm. RoboCup 4-Legged League. Context: Mobile Robots. Sony Aibo Robots 4 vs. 4 robots play fully autonomously Soccer Games. RoboCup @ home. real-world applications

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Panoramic

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  1. Panoramic University of Amsterdam Informatics Institute

  2. Localization

  3. with a Sony Aibo by Jürgen Sturm

  4. RoboCup 4-Legged League Context: Mobile Robots Sony Aibo Robots 4 vs. 4 robots play fully autonomously Soccer Games

  5. RoboCup @ home real-world applications human-machine interaction Fully autonomous robots have to master challenges in unknown & unstructured environments Follow a human, navigate, etc.

  6. Traditional approaches • Aibos / 4-Legged league uses landmarks with known positions, known shape and known color (manually calibration taking hours) • General solutions (SLAM) use better hardware • Laser range finders • Omnidirectional cameras • Robots with better odometry (wheels) The problem:Mobile robot localization(estimating the robot’s position)

  7. Features of new approach • Real-time localization on a Sony Aibo • Take advantage of natural features of a room • Independency of artificial landmarks • Auto-calibrating in new environments • Idea: • Learn a panoramic model of the surroundings of the robot for localization

  8. Color clustering Raw image (208x160, YCbCr) Collect interesting colors (around the robot) Determine 10 most characteristic colors (using an EM clustering algorithm)

  9. Sector appearance Approach Building an virtual panoramic wall Raw image (208x160, YCbCr) Divide in vertical slices, called sectors (360° correspond to 80 sectors) Count color transitions per sector (between the 10 most char- acteristic colors of the scene)

  10. Learning the panorama model Image features (10-12 sectors/image, 10x10 frequencies/sector) Learn panorama model (estimate frequency distributions per sector) Panorama model (80 sectors, 10x10 distributions, each defined by 5 bins)

  11. Alignment and Localization Robot rotated 45° to the left After learning from 131 frames Image features (10-12 sectors/image, 10x10 frequencies/sector) Align with stored panorama model (find shortest path) Output (Rotational estimate Signal-to-noise ratio Confidence range)

  12. Experiments in human environments • Rotational test in living room (at night) Results Learning of the appearance of unknown & unstructured environments

  13. Translational test on soccer fields Human soccer field, outdoors, single learned spot 4-Legged soccer field, indoors, single learned spot

  14. Multi-spot learning • Aibo trained on 4 different spots, yielding 4 different panoramas • Aibo kidnapped and placed back at arbitrary positions on the field • Aibo tries to walk back to center spot

  15. Possibilities for the 4-Legged league • Getting rid of all artificial landmarks • 11 vs. 11 games (bigger field) • Outdoor demonstrations become possible Conclusions

  16. Possible usage for theRoboCup @ home league • Distinguish living room from kitchen or garden • Rough but quick map building • Find relative position of the TV/stove/etc on this map

  17. Other applications • CareBot: navigation in a closed indoor environment • Mobile applications (for example on cellular phones) for quick positional estimates (tourism)

  18. Rotational estimate and Confidence range in numbers

  19. Sector-based feature extraction Camera images Align with panorama model .. .. Orientation buffer 0º and SNR 90º 270º Odometry updates + confidence range 180º Architecture Overview

  20. Conclusions • Accurate estimate of the rotation from a single learned spot (up to 40 meters) • A good estimate of the relative distance from a single learned spot (up to 40 meters) • Rough estimate of the absolute position from multiple trained spots

  21. Panoramic Localization with a Sony Aibo by Jürgen Sturm University of Amsterdam Informatics Institute • User manual • Head button always resets robot and triggers autoshutter & color clustering • Press front button to manually trigger color clustering • In training mode: • Press middle button to start learning of the first spot • Press middle button again to continue learning on more spots • Press back button to switch to localization mode • In localization mode: • Press front button to switch between rotational and translational mode • Press middle button to reset panorama and start learning • Press back button to switch between find and set-reference mode 1 7 2 6 1 4 1 5 8 3 9 Fullly working memorystick image can be downloaded from http://staff.science.uva.nl/~jsturm/panorama/panorama-release.zip

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