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Robots!. Zach Dodds June 16, 2010. Cheap Robots. autonomy. expense. automation. ... vs. autonomy. Autonomous robotics ~ Brazil. autonomous vehicles. DARPA grand challenge. iRobot Roomba ~ 2002. 4,000,000+ vacuums. 3,000+ PackBots. wide-audience robots?. another iRobot founder. - PowerPoint PPT Presentation
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Robots!
Zach Dodds June 16, 2010
Cheap Robots
expense
auto
nom
y
automation...
... vs. autonomy
Autonomous robotics ~ Brazil
autonomous vehicles
DARPA grand challenge
iRobot Roomba ~ 2002
wide-audience robots?
4,000,000+ vacuums 3,000+ PackBots
iRobot Roomba ~ 2002 Rodney Brooks
another iRobot founder
4,000,000+ vacuums 3,000+ PackBots
similar revenue!
Today's capabilities
~~personal robots
personal computers
next-generation applications
Key challenge:
spatial reasoning
but local, not global
the 2000's saw almost all of our progress in robot mapping and navigation
CMU's Boss
Primary sensor: the laser
Personal lasers?
Spatial reasoning today
one view or
two?
Laser Range Scanners
$2000 - $10000 and up $200 - $10 and down
Webcameras
Laser vs. Pixels
Laser Range Scanners
$2000 - $10000 and up $200 - $10 and down
Webcameras
Laser vs. Pixels: Data
Laser Range Scanners
$2000 - $10000 and up
Laser vs. Pixels: Data
Laser Range Scanners
$2000 - $10000 and up $200 - $10 and down
WebcamerasBeautifu
l Data!
More data -- but
more "tangled."
Untangling pixels: 3d from 2d
Where does the additional information come from?
Untangling pixelsIdeas for extracting 3d data from 2d
images?
Untangling pixelsIdeas for extracting 3d data from 2d
images?
via multiple images via image context
image 1 image 2
(x,y,)Nearby
Far off
feature matches
K. Wnuk, '05
+ texturepoint cloud
Microsoft's Photosynt
h
Is anything missing from these 3d reconstructions?see links...
Doing less with less!
Strategy for robots:
a visual compass from 1d image matching: Devin S., 2008
frame 470 frame 485
Where's the ground in front of
me?
intensity profiles did not work
Trickier than it may seem!
Where's the ground in front of
me?
Trickier than it may seem!
Artist Julian Beever
Texture-based 3d: Make3d
textures ~ 524 components
Not feeling so bad... the platform
depth!
Texture-based 3d: Make3dSaxena and Ng 2006
RC car control!
C. Plagemann et al., 2008
Laser-scan data from images?
robot platform "omnicam" images errors...
Best algorithm ~ 1 meter error
Laser-scan data from images?
robot platform "omnicam" images errors...
Our investigation in 2009
Image-patches estimating dense ranges
100 hand-labeled imageslearned textures
travers
able
untr
avers
able
Our approach: 2009
deep belief networkfeedforward net
with backpropagation
Results of groundplane learning
where/why would feature-matching reconstruction fail in these examples...?
Segmentation
Image height ~ distances?
automatic (blue) vs. “correct” (red and green) segmentations
How do these
relate?
What will be the effects of
mis-segmenting?
good news and bad news?
Accuracy
automatic (blue) vs. “correct” (red and green) segmentations
9-image trial setMedian error < 10cmMean error > 500cm!
Opportunity!
"drop outs" and "drop ins"
PixelLaser True scan
I <3 Cheap Robots!
• research challenges
• fun projects!• broad application reach
auto
nom
y
audience
Preview of coming sensor attractions
will it work...?will it replace vision...?
The future is small... ?DARPA will be ready if it's true.
See obstacles around them
Powerslide
another...
Fly through windows
A. Amert, N. M'Tarrah, D. Halloran '10
class projects
camera?
class projects
M. Badger and C. Gaudet '10
B. Green, K. Burgers, S. Lakhani '11
in core Biology/CS: M. Morabe and T. Luke '13
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...
Autonomous Vehicles : Fall
2010
student-run FIRST
scrimmage
energy autonomy
3d from 2d
laser range finder
camera
More is possible! How...
3d from 2d
laser range finder
camera
More is possible! How...
packbot parts