TechUnited Robocup – the object tracking problem René van de Molengraft MSL Workshop, Stuttgart,...
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Transcript of TechUnited Robocup – the object tracking problem René van de Molengraft MSL Workshop, Stuttgart,...
![Page 1: TechUnited Robocup – the object tracking problem René van de Molengraft MSL Workshop, Stuttgart, November 5/6 th, 2009.](https://reader038.fdocuments.in/reader038/viewer/2022110403/56649e6b5503460f94b69350/html5/thumbnails/1.jpg)
TechUnited Robocup –
the object tracking problemRené van de Molengraft
MSL Workshop, Stuttgart, November 5/6th , 2009
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Looking back• No joint paper
• two-day visits Stuttgart and Tribots
• Special issue IFAC Mechatronics on MSL
• EU-Project RoboEarth
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Evolution and revolution
Devbot0 2005
Model 2006
Model 2007
Model 2008
Model 2009
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• 5 against 5
• >30 kg, 4 m/s
• omni-vision: 30 x 640 x 480 x 3 bytes/s
• assume features (x, y, r) from omni-vision
• noise (shadow, motion blur, finite resolution) and multiple features/object
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On the field
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Object tracking
12
time 1
12
time 2
• data association
• find best state estimates given past position measurements
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Combinatorial explosion
12
534
6
• at time t1: m1 measurements, n1 objects
• m1*n1 possible associations
• at next time t2: m1*n1*m2*n2
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State estimation
• 2-D position measurements from vision
• constant velocity model
• stationary Kalman filter
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Object model
Cqy
Aqq
01
00
00
01 ,
0000
1000
0000
0010
, CA
v
y
v
x
q
y
x
• zero-acceleration
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Kalman filter
vyz
qCzKqAq
)ˆ(ˆ̂
• choose uncertainty weigths
• solve Riccati equation -> K
• variance equation is disregarded
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Sequential clustering using Kalman filters
• inspired by Schubert and Sidenbladh, Sequential clustering with particle filters – estimating the number of clusters from data, 2005
• prune exploding tree by particle filtering
• static objects only
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Sequential clustering using Kalman filters
• prune exploding tree by discrete filtering
• track dynamic objects
• incorporate a-priori knowledge
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Sequential clustering using Kalman filters
• measurements are processed sequentially
• measurement can be associated with
• clutter
• new object
• existing object
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Tree with all possible associations
[]
[0] [1]
[0,0] [0,1] [1,0] [1,1] [1,2]
measurement 1
measurement 2
• 1, 2, 5, 52, 203, 877, 4140, 21147, 115975, 678570
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Hypothesis H
• e.g. H= [0, 1, 1, 2, 1, 2, 3, 0, 1, 1, 1, 4, 2]
• so, 4 objects -> 4 Kalman filters
• each hypothesis is assigned a probability
• propagate hypotheses via Bayesian filtering
)|()|(
)|()|()|( 11
1
1
kkkk
kkkkkk ZHP
ZzP
HHPHzPZHP
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Prediction )|( 1kk HHP
02.0 ,01.0 ..
/)1(
cge
nPPP
PP
cP
objnewclutterexisting
existingnew
clutter
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Correction )|( kk HzP
kqC ˆkz
corrP
• is the position of the object in assigned to measurement k
kHkqC ˆ
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Pruning by discrete filter
• set of hypotheses form a discrete distribution
• select n hypotheses with highest probability
• throw away hypotheses with P<0.01*Pmax
• tree is pruned to at most n hypotheses
• n = 10 still works for 10 to 20 objects!
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A-priori knowledge
• region of interest
• clip beyond boundary in state space
• measurements
• objects in hypotheses• maximum number of objects in hypothesis
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• Maximum a Posteriori (MAP) estimate
• states
Best estimate
)(max PHH kP
MAPk
objn jkq ,ˆ
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Implementation
• Simulink S-function in C
• cpu effort
• linear in #measurements
• linear in #hypotheses
• quadratic in #objects
• < 5 ms (10, 20, 10)
• add predictor for anticipation