A Probabilistic Approach to Collaborative Multi-robot Localization
Robot Compagnion Localization at home and in the office
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Robot Compagnion Robot Compagnion Localization Localization
at home and in the officeat home and in the office
Arnoud Visser, Arnoud Visser, Jürgen SturmJürgen Sturm, , Frans GroenFrans Groen
University of AmsterdamInformatics Institute
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OverviewOverview Mobile roboticsMobile robotics
Robot localizationRobot localization
Presentation of the panorama approachPresentation of the panorama approach
ResultsResults
Demonstration videosDemonstration videos
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Mobile roboticsMobile robotics
SICO at Kosair Children's Hospital Dometic, Louisville, Kentucky
Sony Aibos playing soccerCinekids, De Balie, Amsterdam
Robot cranes and trucks unloading shipsPort of Rotterdam
RC3000, the robocleanerKärcher
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The localization problemThe localization problem
Robot localizationRobot localization.. is the problem of estimating the robot’s .. is the problem of estimating the robot’s
pose relative to a map of the pose relative to a map of the environment.environment.
Position trackingPosition tracking Global localizationGlobal localization Kidnapping problemKidnapping problem
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LocalizationLocalization
SensorsSensors Odometry, GPS, Laserscanner, Camera..Odometry, GPS, Laserscanner, Camera.. Feature spaceFeature space
World representationWorld representation Topological graphs, grid-based mapsTopological graphs, grid-based maps
FiltersFilters Kalman filters, particle filtersKalman filters, particle filters
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Classical approachesClassical approaches
Special environmentsSpecial environments (Visual) landmarks(Visual) landmarks (Electro-magnetic) guiding lines(Electro-magnetic) guiding lines
Special sensorsSpecial sensors GPSGPS Laser-scannersLaser-scanners Omni-directional camerasOmni-directional cameras
Special requirementsSpecial requirements Computationally heavy (offline Computationally heavy (offline
computation)computation)
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New approachNew approach
Natural environmentsNatural environments Human environmentsHuman environments Unstructured and/or unknown for the Unstructured and/or unknown for the
robotrobot Normal sensorsNormal sensors
CameraCamera Reasonable requirementsReasonable requirements
Real-timeReal-time Moderate hardware requirementsModerate hardware requirements
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Platform: Sony AiboPlatform: Sony Aibo
Internal camera•30fps•208x160 pixels
Computer•64bit RISC processor•567 MHz•64 MB RAM•16 MB memorystick•WLAN
Actuators•Legs: 4 x 3 joints•Head: 3 joints
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Demo: CompassDemo: Compass
Library, University of Amsterdam
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SynopsisSynopsis
.. .... ..
Raw image Color class imageSector-based
feature extraction
Previously learnedworld panoramafor a given spot
Alignment step
Odometry data Post filtering
rotation, translation,confidence range
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Color segmentationColor segmentation
Sidetrack: Color Sidetrack: Color CalibrationCalibration
Robot collects colors Robot collects colors from environmentfrom environment
Colors are clustered Colors are clustered using an EM algorithmusing an EM algorithm
Color-to-Colorclass Color-to-Colorclass lookup table is created lookup table is created for faster accessfor faster access
Raw image
Color class image
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MathematicsMathematics
rotation
translation
feature vector
ideal world model
learned world model
360;..;0[for
,
RRfP
TRfP
f
T
R
spot
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Feature space conversionFeature space conversion
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Feature vectors and Feature vectors and world modelworld model
World model distribution
Feature vector consists of color transition counts between the n color classes
nnn
n
ff
ff
f
1
111
n
i
n
jijspotspot RfPRfP
1 1
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Feature space conversion Feature space conversion (2)(2)
Raw imageColor class
imageSector-based
feature vectors
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LearningLearning
)1(
12-2
-11
1 22 if
22 if
2 if
1
binsij
-binsbinsij
ijij
ijij
bins
k
kij
ijspot
fm
fm
fm
mRfP
Update distribution of single color class transition
by updating the constituting counters bins
ijij mm ;...;1
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MatchingMatching
Likelihood ofSingle sector
Rotation estimate
Confidence estimate
sssspot
R
sssspot
sssspot
R
sssspot
spot
RRfP
RRfPC
RRfPR
RfP
RfP
mean
ˆˆ
maxargˆ
Adjacent sectors
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Post-processing: Post-processing: CompassCompass
Idea: smooth rotational estimate over Idea: smooth rotational estimate over multiple framesmultiple frames
+ removes outliers+ removes outliers
+ stabilizes estimate+ stabilizes estimate
+ integrates (rotational) odometry+ integrates (rotational) odometry
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Results: CompassResults: Compass
Brightly illuminated living room
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Results: CompassResults: Compass
Daylight office environment
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Results: CompassResults: Compass
Outdoor soccer field
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Results: CompassResults: Compass
Robocup 4-Legged soccer field
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Signal degradation (w.r.t. Signal degradation (w.r.t. distance)distance)
Robocup 4-Legged soccer field
0
10
20
30
40
50
60
70
80
90
0 50 100 150 200 250Distance from learned spot (centimeters)
deg
rees
confidence range
error in rotation estimate
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Post-processing: Grid Post-processing: Grid localizationlocalization
Idea: learn multiple spots, then use Idea: learn multiple spots, then use confidence value to estimate the confidence value to estimate the robot‘s position in betweenrobot‘s position in between
– – fixed grid fixed grid (better: self-learned graph based on (better: self-learned graph based on
confidence)confidence)
– – difficult to integrate odometrydifficult to integrate odometry
+ proof of concept+ proof of concept
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Demo: Grid localizationDemo: Grid localization
Robocup 4-Legged soccer field
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Results: Grid localizationResults: Grid localization
Robocup 4-Legged soccer field
-100
-75
-50
-25
0
25
50
75
100
-100 -75 -50 -25 0 25 50 75 100
x [cm]
y [c
m] Positioning
accuracy
cm
cm
cm
cm
37.15
73.16
09.12
30.22
Robot walks back to center after kidnap
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ConclusionsConclusions Novel approach to localization:Novel approach to localization:
Works in unstructured Works in unstructured environmentsenvironments
Tested on various locationsTested on various locations
Interesting approach for mobile Interesting approach for mobile robots robots at home and in the officeat home and in the office
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Questions?Questions?