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ParticleFilterLocalization
forAutonomousAUVs
UsingAugmentedRealityTags
Ed Kelley, 2013
Szymon Rusinkiewicz
aka
Where is the
Quadcopter?
Motivation
This
statue
is
cool
http://www.asergeev.com/pictures/archives/2007/572/jpeg/05.jpg
Iwanta3dmodel
http://www.asergeev.com/pictures/archives/2007/572/jpeg/05.jpg
Video Games
Virtual Reality
Movies
Archeology
Architecture
Maps
Crash Scenes
Manual Modeling?
Laser Scanner?
Multi-View Stereo?
MicrosoftKinect?
Manual Modeling?
Laser Scanner?
Multi-View Stereo?
MicrosoftKinect?
Irschara et al. 2010
Easy
Cheap
Complete
HighQuality
Quadcopters!
RelatedWork
Irschara et al.
Engel et al.
Bills et al.
SystemDesign
AR.Drone2.0
http://ardrone2.parrot.com/photos/photo-album/
Localization+Controller=Autonomy
Localization
Local
drift.
tend
to
measurements
NoGPS
Norangefinders
Kalman Filter?
Grid Based Markov?
ParticleFilter?
Kalman Filter?
Grid Based Markov?
ParticleFilter?
. Thisis
aparticle
. Itrepresents
apossible
pose
. x
y
z
heading
weight
PredictionStepUpdate the position of each particle
using noisy velocity and gyroscope
readings.
CorrectionStep
1. Check for an augmented reality tag.
2. Calculate transformation from
camera to tag.
3. Use known coordinates of the tag to
calculate the position of the quadcopter.
CorrectionStep
4. Weight the particles using their similarity
to this calculated position.
5. Perform weighted resampling of the
particles.
6. With some probability, replace particles
with this calculated position.
Estimate
Use a linear combination of the particle
values to create an estimated pose.
Testing
Gyroscope
Ultrasound
AR Tag
AR Tag
AR Tag
AR Tag
ManualFlightTest
Conclusion
Particlefilterlocalizationusing
augmentedrealitytags
performssubstantiallybetter
thanintegratedvelocityalone.
ARTagsare
highlydependent
onlighting.
ItscalledHardware
forareason.
Next steps...
Fullintegrationwithcontroller.
Modelingobjects.
Thanks!

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Particle Filter Localization for AUVs