\label{sec:sensors}
\bi{Meas. Model}: $\vec{z} = \vec{h}(\vec{x}) + \vec{v} + \vec{o}$, with $\vec{h}(\vec{x})$ determ. mean,
$\vec{v}$ zero-mean noise, $\vec{o}$ unmodelled effects, $\vec{x}$ true state.
$\vec{h}(\vec{x})$ describes how to compute $x$ from known values (e.g. Intercept-Theorem).
Probabilistic M.M: use random variable in definition, and s. \ref{sec:error-propagation}

\bi{Motor encoders} Typ. 64-2048 increments per rev; Estimate rot

\bi{Rolling-Shutter} Most CMOS sensors don't take full image at once, need time stamp for each row

\shortdefinition[Proprioceptive] Robot-internal states (e.g. IMU, encoders)

\shortdefinition[Exteroceptive] Environment (e.g. LIDAR, Cameras)