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Robots! PowerPoint Presentation

Robots!

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Robots!

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  1. Robots! Zach Dodds June 16, 2010

  2. Cheap Robots autonomy expense

  3. automation... ... vs. autonomy

  4. Autonomous robotics ~ Brazil

  5. autonomous vehicles DARPA grand challenge

  6. iRobot Roomba ~ 2002 4,000,000+ vacuums 3,000+ PackBots wide-audience robots?

  7. another iRobot founder iRobot Roomba ~ 2002 Rodney Brooks 4,000,000+ vacuums 3,000+ PackBots similar revenue!

  8. ~ ~ Today's capabilities personal computers personal robots

  9. next-generation applications

  10. Key challenge: spatial reasoning but local, not global

  11. Primary sensor: the laser CMU's Boss the 2000's saw almost all of our progress in robot mapping and navigation Personal lasers?

  12. Spatial reasoning today one view or two?

  13. Laser vs. Pixels Laser Range Scanners Webcameras $200 - $10 and down $2000 - $10000 and up

  14. Laser vs. Pixels: Data Laser Range Scanners Webcameras $200 - $10 and down $2000 - $10000 and up

  15. Laser vs. Pixels: Data Laser Range Scanners $2000 - $10000 and up

  16. More data -- but more "tangled." Beautiful Data! Laser Range Scanners Webcameras $200 - $10 and down $2000 - $10000 and up

  17. Untangling pixels: 3d from 2d Where does the additional information come from?

  18. Untangling pixels Ideas for extracting 3d data from 2d images?

  19. Untangling pixels Ideas for extracting 3d data from 2d images? Far off image 1 image 2 Nearby (x,y,q) via multiple images via image context

  20. feature matches + texture point cloud K. Wnuk, '05

  21. Microsoft's Photosynth

  22. Is anything missing from these 3d reconstructions? see links...

  23. Strategy for robots: Doing less with less!

  24. frame 470 frame 485 a visual compass from 1d image matching: Devin S., 2008

  25. Where's the ground in front of me? intensity profiles did not work Trickier than it may seem!

  26. Artist Julian Beever Where's the ground in front of me? Trickier than it may seem!

  27. Texture-based 3d: Make3d Not feeling so bad... the platform textures ~ 524 components depth!

  28. Texture-based 3d: Make3d Saxena and Ng 2006 RC car control!

  29. Laser-scan data from images? robot platform "omnicam" images errors... C. Plagemann et al., 2008

  30. Laser-scan data from images? robot platform "omnicam" images errors... Best algorithm ~ 1 meter error

  31. Our investigation in 2009 Image-patches estimating dense ranges

  32. Our approach: 2009 untraversable traversable learned textures 100 hand-labeled images

  33. feedforward net with backpropagation deep belief network Results of groundplane learning where/why would feature-matching reconstruction fail in these examples...?

  34. Segmentation

  35. Image height ~ distances? How do these relate? What will be the effects of mis-segmenting? good news and bad news? automatic (blue) vs. “correct” (red and green) segmentations

  36. Accuracy 9-image trial set Median error < 10cm Mean error > 500cm! automatic (blue) vs. “correct” (red and green) segmentations

  37. Opportunity! PixelLaser True scan "drop outs" and "drop ins"

  38. I <3 Cheap Robots! autonomy audience • research challenges • broad application reach • fun projects!

  39. Preview of coming sensor attractions will it work...? will it replace vision...?

  40. The future is small... ? DARPA will be ready if it's true.

  41. Fly through windows See obstacles around them Powerslide another...

  42. class projects camera? A. Amert, N. M'Tarrah, D. Halloran '10

  43. class projects

  44. M. Badger and C. Gaudet '10

  45. B. Green, K. Burgers, S. Lakhani '11

  46. in core Biology/CS: M. Morabe and T. Luke '13

  47. Please don't eat the wheels. You don't know where they've been. The name "Nutty" derives from our original plan to use peanut butter cookies as wheels. However, we were too grossed out by the smell of day-old peanut butter cookies, so we opted for other types...

  48. student-run FIRST scrimmage Autonomous Vehicles : Fall 2010 energy autonomy