Velocity Motion Model

tags: Odometry Motion Model, Motion Model With Maps

The velocity motion model assumes that the robot is controlled through 2 velocities: rotational and translational velocity:

$u_{t} = (\begin{array}{l} v_{t} ω_{t} \end{array})$

A sampling algorithm generates a random pose according to the distribution $p (x_{t} | u_{t}, x_{t - 1})$ , and perturbs it with the noise, drawn from the error parameters of the kinematic motion model.

$(\begin{matrix} \hat{v} \hat{ω} \end{matrix}) = (\begin{matrix} v ω \end{matrix}) + (\begin{matrix} ε_{α_{1} | v | + α_{2} | ω |} ε_{α_{3} | v | + α_{4} | ω |} \end{matrix})$

Jethro's Braindump

Velocity Motion Model

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