Day 017_Part_1

Day 017_Part_1 - x t-1 find the new pose x t Sampling from...

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Motion Models (cont) 2/13/2012 1
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Sampling from the Velocity Motion Model 2/13/2012 2 suppose that a robot has a map of its environment and it needs to find its pose in the environment this is the robot localization problem (Chapter 7) several variants of the problem the robot knows where it is initially the robot does not know where it is initially kidnapped robot: at any time, the robot can be teleported to another location in the environment a popular solution to the localization problem is the particle filter (Chapter 4) uses simulation to sample the state density ) , | ( 1 t t t x u x p
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Sampling from the Velocity Motion Model 2/13/2012 3 sampling the conditional density is easier than computing the density because we only require the forward kinematics model given the control u t and the previous pose
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Unformatted text preview: x t-1 find the new pose x t Sampling from the Velocity Motion Model 2/13/2012 4 c c y x = − θ y x x t 1 ? = ′ ′ ′ = y x x t t ∆ ω v v r = cos sin v y y v x x c c + = − = Eqs 5.7, 5.8 Sampling from the Velocity Motion Model 2/13/2012 5 ∆ ∆ + − ∆ + + − + = ∆ + ∆ + − ∆ + + = ′ ′ ′ t t t y x t t y t x y x v v v v v c v c ω θ ) cos( cos ) sin( sin ) cos( ) sin( Eqs 5.9 *we already derived this for the differential drive!...
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This note was uploaded on 02/13/2012 for the course CSE 4421 taught by Professor Burton during the Winter '11 term at York University.

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Day 017_Part_1 - x t-1 find the new pose x t Sampling from...

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