Tutorial on Particle Filters assembled and extended by Longin Jan Latecki Temple University, [email protected] using slides from. Keith Copsey, Pattern and Information Processing Group, DERA Malvern; D. Fox, J. Hightower, L. Liao, D. Schulz, and G. Borriello, Univ. of Washington, Seattle
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assembled and extended by Longin Jan Latecki
Temple University, [email protected]
using slides from
Keith Copsey, Pattern and Information Processing Group, DERA Malvern;
D. Fox, J. Hightower, L. Liao, D. Schulz, and G. Borriello,
Univ. of Washington, Seattle
Honggang Zhang, Univ. of Maryland, College Park
Miodrag Bolic, University of Ottawa, Canada
Michael Pfeiffer, TU Gratz, Austria
–Predict next state pdf from current estimate
–Update the prediction using sequentially arriving new measurements
Global Localization of Robot with Sonarhttp://www.cs.washington.edu/ai/Mobile_Robotics/mcl/animations/global-floor.gif
Recall “law of total probability” (or marginalization)
and “Bayes’ rule”
How to use it? What else to know?
Step 1: updating
Step 4: predicting
Step 2: predicting
Example 1 (continue)
are normalized weights.
SIS Particle Filter Algorithm
Draw a particle
Assign a weight
(k is index over time and i is the particle index)
Our goal is to expand p and q in time t
and under Markov assumptions
We obtain then
Step 4: predicting.
Predict the new locations of particles.
Step 2: predicting.
Predict the new locations of particles.Example 2: Particle Filter
Particles are more concentrated in the region where the person is more likely to be
–e.g. human motion (body parts)
–e.g. mapping gold price to stock price
SIS Particle Filter Algorithm (p. 27)
Basic SIR Particle Filter algorithm (p. 39,40)
Generic SIR Particle Filter algorithm (p. 42)