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Transcript of Recursive Bayes Filtering Advanced AI Wolfram Burgard.
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Recursive Bayes Filtering
Advanced AI
Wolfram Burgard
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RoboCup
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Physical Agents are Inherently Uncertain
Uncertainty arises from four major factors: Environment stochastic, unpredictable Robot stochastic Sensor limited, noisy Models inaccurate
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Nature of Sensor Data
Odometry Data Range Data
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Probabilistic Techniques for Physical Agents
Key idea: Explicit representation of uncertainty using the calculus of probability theory
Perception = state estimationAction = utility optimization
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Advantages of Probabilistic Paradigm
Can accommodate inaccurate models Can accommodate imperfect sensors Robust in real-world applications Best known approach to many hard
robotics problems
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Pitfalls
Computationally demanding False assumptions Approximate
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Simple Example of State Estimation
Suppose a robot obtains measurement z What is P(open|z)?
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Causal vs. Diagnostic Reasoning
P(open|z) is diagnostic. P(z|open) is causal. Often causal knowledge is easier to
obtain. Bayes rule allows us to use causal
knowledge:
)()()|(
)|(zP
openPopenzPzopenP
count frequencies!
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Example
P(z|open) = 0.6 P(z|open) = 0.3 P(open) = P(open) = 0.5
67.03
2
5.03.05.06.0
5.06.0)|(
)()|()()|(
)()|()|(
zopenP
openpopenzPopenpopenzP
openPopenzPzopenP
• z raises the probability that the door is open.
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Combining Evidence
Suppose our robot obtains another observation z2.
How can we integrate this new information?
More generally, how can we estimateP(x| z1...zn )?
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Recursive Bayesian Updating
),,|(
),,|(),,,|(),,|(
11
11111
nn
nnnn
zzzP
zzxPzzxzPzzxP
Markov assumption: zn is independent of z1,...,zn-1 if we know x.
)()|(
),,|()|(
),,|(
),,|()|(),,|(
...1...1
11
11
111
xPxzP
zzxPxzP
zzzP
zzxPxzPzzxP
ni
in
nn
nn
nnn
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Example: Second Measurement
P(z2|open) = 0.5 P(z2|open) = 0.6
P(open|z1)=2/3
625.08
5
31
53
32
21
32
21
)|()|()|()|(
)|()|(),|(
1212
1212
zopenPopenzPzopenPopenzP
zopenPopenzPzzopenP
• z2 lowers the probability that the door is open.
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A Typical Pitfall
Two possible locations x1 and x2
P(x1)= 1-P(x2)= 0.99 P(z|x2)=0.09 P(z|x1)=0.07
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
5 10 15 20 25 30 35 40 45 50
p( x
| d)
Number of integrations
p(x2 | d)p(x1 | d)
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Actions
Often the world is dynamic since actions carried out by the robot, actions carried out by other agents, or just the time passing by
change the world.
How can we incorporate such actions?
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Typical Actions
The robot turns its wheels to move The robot uses its manipulator to grasp
an object Plants grow over time…
Actions are never carried out with absolute certainty.
In contrast to measurements, actions generally increase the uncertainty.
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Modeling Actions
To incorporate the outcome of an action u into the current “belief”, we use the conditional pdf
P(x|u,x’)
This term specifies the pdf that executing u changes the state from x’ to x.
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Example: Closing the door
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State Transitions
P(x|u,x’) for u = “close door”:
If the door is open, the action “close door” succeeds in 90% of all cases.
open closed0.1 1
0.9
0
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Integrating the Outcome of Actions
')'()',|()|( dxxPxuxPuxP
)'()',|()|( xPxuxPuxP
Continuous case:
Discrete case:
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Example: The Resulting Belief
)|(1161
83
10
85
101
)(),|(
)(),|(
)'()',|()|(
1615
83
11
85
109
)(),|(
)(),|(
)'()',|()|(
uclosedP
closedPcloseduopenP
openPopenuopenP
xPxuopenPuopenP
closedPcloseduclosedP
openPopenuclosedP
xPxuclosedPuclosedP
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Bayes Filters: Framework
Given: Stream of observations z and action data u:
Sensor model P(z|x). Action model P(x|u,x’). Prior probability of the system state P(x).
Wanted: Estimate of the state X of a dynamical system. The posterior of the state is also called Belief:
),,,|()( 121 tttt zuzuxPxBel
},,,{ 121 ttt zuzud
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Markov Assumption
Underlying Assumptions Static world Independent noise Perfect model, no approximation errors
)|,...,(),...,,,|,...,( 11211 ttttttt xddpdddxddp
)|,...,,(...),,,|,...,,( 012101 tttttttt xdddpddxdddp
Xt-1 Xt Xt+1
Zt-1 Zt Zt+1
ut-1 ut
),|(),...,,,|( 110211 ttttttt xuxpddxuxp
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Bayes Filters
),,,|(),,,,|( 121121 ttttt uzuxPuzuxzP Bayes
z = observationu = actionx = state
),,,|()( 121 tttt zuzuxPxBel
Markov ),,,|()|( 121 tttt uzuxPxzP
1111 )(),|()|( ttttttt dxxBelxuxPxzP
Markov1121111 ),,,|(),|()|( tttttttt dxuzuxPxuxPxzP
11211
1121
),,,|(
),,,,|()|(
ttt
ttttt
dxuzuxP
xuzuxPxzP
Total prob.
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Bayes Filter Algorithm
1. Algorithm Bayes_filter( Bel(x),d ):2. h=0
3. if d is a perceptual data item z then4. For all x do5. 6. 7. For all x do8.
9. else if d is an action data item u then10. For all x do11.
12. return Bel’(x)
)()|()(' xBelxzPxBel )(' xBel
)(')(' 1 xBelxBel
')'()',|()(' dxxBelxuxPxBel
1111 )(),|()|()( tttttttt dxxBelxuxPxzPxBel
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Bayes Filters are Familiar!
Kalman filters Particle filters Hidden Markov models Dynamic Bayes networks Partially Observable Markov Decision
Processes (POMDPs)
1111 )(),|()|()( tttttttt dxxBelxuxPxzPxBel
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Representations for Bayesian Robot Localization
Discrete approaches (’95)• Topological representation (’95)
• uncertainty handling (POMDPs)• occas. global localization, recovery
• Grid-based, metric representation (’96)• global localization, recovery
Multi-hypothesis (’00)• multiple Kalman filters• global localization, recovery
Particle filters (’99)• sample-based representation• global localization, recovery
Kalman filters (late-80s?)• Gaussians• approximately linear models• position tracking
AI
Robotics
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What is the Right Representation?
Kalman filters Multi-hypothesis tracking Grid-based representations Topological approaches Particle filters
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Gaussians
2
2)(
2
1
2
2
1)(
:),(~)(
x
exp
Nxp
-
Univariate
)()(2
1
2/12/
1
)2(
1)(
:)(~)(
μxΣμx
Σx
Σμx
t
ep
,Νp
d
m
Multivariate
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59
Kalman Filters
Estimate the state of processes that are governed by the following linear stochastic difference equation.
The random variables vt and wt represent the process measurement noise and are assumed to be independent, white and with normal probability distributions.
ttt
tttt
wCxz
vBuAxx
1
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[Schiele et al. 94], [Weiß et al. 94], [Borenstein 96],
[Gutmann et al. 96, 98], [Arras 98]
Kalman Filters
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Kalman Filter Algorithm
1. Algorithm Kalman_filter( < ,S>, d ):
2. If d is a perceptual data item z then3. 4. 5.
6. Else if d is an action data item u then7. 8.
9. Return < ,S>
SS )( KCI
1SSS obs
TT CCCK)( CzK
actTAA SSS
BuA
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Non-linear Systems
Very strong assumptions: Linear state dynamics Observations linear in state
What can we do if system is not linear? Linearize it: EKF Compute the Jacobians of the dynamics
and observations at the current state. Extended Kalman filter works surprisingly
well even for highly non-linear systems.
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[Gutmann et al. 96, 98]:
Match LRF scans against map
Highly successful in RoboCup mid-size league
Kalman Filter-based Systems (1)
Courtesy of S. Gutmann
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Kalman Filter-based Systems (2)
Courtesy of S. Gutmann
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[Arras et al. 98]:
Laser range-finder and vision
High precision (<1cm accuracy)
Kalman Filter-based Systems (3)
Courtesy of K. Arras
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[Cox 92], [Jensfelt, Kristensen 99]
Multi-hypothesisTracking
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Belief is represented by multiple hypotheses
Each hypothesis is tracked by a Kalman filter
Additional problems:
Data association: Which observation
corresponds to which hypothesis?
Hypothesis management: When to add / delete
hypotheses?
Huge body of literature on target tracking, motion
correspondence etc.
Localization With MHT
See e.g. [Cox 93]
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[Jensfelt and Kristensen 99,01]
Hypotheses are extracted from LRF scans Each hypothesis has probability of being the
correct one:
Hypothesis probability is computed using Bayes’ rule
Hypotheses with low probability are deleted
New candidates are extracted from LRF scans
MHT: Implemented System (1)
)}(,,ˆ{ iiii HPxH S
},{ jjj RzC
)(
)()|()|(
sP
HPHsPsHP ii
i
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MHT: Implemented System (2)
Courtesy of P. Jensfelt and S. Kristensen
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MHT: Implemented System (3)Example run
Map and trajectory
# hypotheses
Hypotheses vs. time
P(Hbest)
Courtesy of P. Jensfelt and S. Kristensen
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[Burgard et al. 96,98], [Fox et al. 99], [Konolige et al. 99]
Piecewise Constant
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Piecewise Constant Representation
),,( > yxxbel t
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Grid-based Localization
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Tree-based Representations (1)
Idea: Represent density using a variant of Octrees
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Xavier: Localization in a Topological Map
[Simmons and Koenig 96]
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Represent density by random samples Estimation of non-Gaussian, nonlinear processes
Monte Carlo filter, Survival of the fittest, Condensation, Bootstrap filter, Particle filter
Filtering: [Rubin, 88], [Gordon et al., 93], [Kitagawa 96]
Computer vision: [Isard and Blake 96, 98] Dynamic Bayesian Networks: [Kanazawa et al., 95]
Particle Filters
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MCL: Global Localization
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MCL: Sensor Update
)|()(
)()|()()|()(
xzpxBel
xBelxzpw
xBelxzpxBel
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MCL: Robot Motion
'd)'()'|()( , xxBelxuxpxBel
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)|()(
)()|()()|()(
xzpxBel
xBelxzpw
xBelxzpxBel
MCL: Sensor Update
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MCL: Robot Motion
'd)'()'|()( , xxBelxuxpxBel
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1. Algorithm particle_filter( St-1, ut-1 zt):
2.
3. For Generate new samples
4. Sample index j(i) from the discrete distribution given by wt-
1
5. Sample from using and
6. Compute importance weight
7. Update normalization factor
8. Insert
9. For
10. Normalize weights
Particle Filter Algorithm
0, tS
ni 1
},{ > it
ittt wxSS
itw
itx ),|( 11 ttt uxxp )(
1ij
tx 1tu
)|( itt
it xzpw
ni 1
/it
it ww
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Resampling
Given: Set S of weighted samples.
Wanted : Random sample, where the probability of drawing xi is given by wi.
Typically done n times with replacement to generate new sample set S’.
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w2
w3
w1wn
Wn-1
Resampling
w2
w3
w1wn
Wn-1
• Roulette wheel
• Binary search, log n
• Stochastic universal sampling
• Systematic resampling
• Linear time complexity
• Easy to implement, low variance
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Motion Model p(xt | at-1, xt-1)
Model odometry error as Gaussian noise on , , a b and d
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Start
Motion Model p(xt | at-1, xt-1)
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Model for Proximity Sensors
The sensor is reflected either by a known or by anunknown obstacle:
Laser sensor Sonar sensor
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[Fox et al., 99]
MCL: Global Localization (Sonar)
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Using Ceiling Maps for Localization
[Dellaert et al. 99]
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Vision-based Localization
P(s|x)
h(x)s
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MCL: Global Localization Using Vision
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Localization for AIBO robots
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Adaptive Sampling
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• Idea: • Assume we know the true belief.• Represent this belief as a multinomial distribution.• Determine number of samples such that we can guarantee
that, with probability (1- d ), the KL-distance between the true posterior and the sample-based approximation is less than e.
• Observation: • For fixed d and e, number of samples only depends on
number k of bins with support:
KLD-sampling
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MCL: Adaptive Sampling (Sonar)
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Particle Filters for Robot Localization (Summary)
Approximate Bayes Estimation/Filtering Full posterior estimation Converges in O(1/#samples) [Tanner’93] Robust: multiple hypotheses with degree of
belief Efficient in low-dimensional spaces: focuses
computation where needed Any-time: by varying number of samples Easy to implement
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Localization Algorithms - Comparison
Kalman filter
Multi-hypothesis
tracking
Topologicalmaps
Grid-based(fixed/variable)
Particle filter
Sensors Gaussian Gaussian Features Non-Gaussian Non-Gaussian
Posterior Gaussian Multi-modal Piecewise constant
Piecewise constant
Samples
Efficiency (memory) ++ ++ ++ -/+ +/++
Efficiency (time) ++ ++ ++ o/+ +/++
Implementation + o + +/o ++
Accuracy ++ ++ - +/++ ++
Robustness - + + ++ +/++
Global localization
No Yes Yes Yes Yes
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Bayes Filtering: Lessons Learned
General algorithm for recursively estimating the state of dynamic systems.
Variants: Hidden Markov Models (Extended) Kalman Filters Discrete Filters Particle Filters