Transcript Probabilistic Robotics

CS226 Statistical Techniques In Robotics
Sebastian Thrun (Instructor) and Josh Bao (TA)
http://robots.stanford.edu/cs226
Office: Gates 154, Office hours: Monday 1:30-3pm
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Warm-Up Assignment: Localization,
Due Sept 23
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Warm-Up Assignment: Localization
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Warm-Up Assignment: Localization
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Bayes Filters
p( xt | d0t , m)  p( xt | zt , ut 1, zt 1,, z0 , m)
x = state
d = data
m = map
t = time
z = observation
u = control
Bayes
  p( zt | xt , ut 1, zt 1,, z0 , m) p( xt | ut 1, zt 1,, z0 , m)
  p( zt | xt , m) p( xt | ut 1, zt 1,, z0 , m)
Markov
  p( zt | xt , m)  p( xt | xt 1 , ut 1 ,, z0 , m) p( xt 1 | ut 1 ,, z0 , m) dxt 1
  p( zt | xt , m)  p( xt | xt 1 , ut 1 ) p( xt 1 | zt 1 , ut 2 , z0 , m) dxt 1
Markov
  p( zt | xt , m)  p( xt | xt 1 , ut 1 ) p( xt 1 | d 0t 1 , m) dxt 1
[Kalman 60, Rabiner 85]
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Nature of Odometry Data
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Probabilistic Kinematics
map m
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Nature of Sensor Data
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Probabilistic Range Sensing
laser data
p(o|s,m)
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Posterior Probability (Single Scan)
observation o
p(o|s,m)
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Grid Approximations
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Markov Localization
in Grid Map
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Monte Carlo Localization
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Sample Approximations
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Monte Carlo Localization, cont’d
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