I'm building a robot with differential drive. I've reached the point when I can drive it around on remote control and I'm trying to get the localization working. Now I would like to exactly measure parameters of the robot.
Model of the robot I'm using has two wheels spaced $b$ meters, each wheel has a distance per encoder tick $s_L$ and $s_R$ and variance standard deviation of the driven distance $\sigma_L$ and $\sigma_R$. When moving, distances are random variables from the following distributions: $d_L \sim t_{L}s_{L}N(1, \sigma_L^2)$ and $d_R \sim t_{R}s_{R}N(1, \sigma_R^2)$. Later this model might expand a little bit.
What is a good way to measure the parameters?
I found a way to measure $b$, $d_L$ and $d_R$ (added that as an answer), but I have no idea how to measure the standard deviations.
The model will be used as a prediction input in MCL, so I don't need covariance matrix for localization.