Probabilistic Robotics. Bayes Filter Implementations Particle filters. Sample-based Localization (sonar). Particle Filters. Represent belief by random samples Estimation of non-Gaussian, nonlinear processes
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Bayes Filter Implementations
Weight samples: w = f / g
Wanted: samples distributed according to p(x| z1, z2, z3)
We can draw samples from p(x|zl) by adding noise to the detection parameters.
draw xit-1from Bel(xt-1)
draw xitfrom p(xt | xit-1,ut-1)
Importance factor for xit:Particle Filter Algorithm
Also called stochastic universal sampling
[Robotics And Automation Magazine, to appear]
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Trajectory of the robot: