[AMR] Notes on tasks, improved layouts

electives/amr/parts/02_Sensors-Actuators/00_intro.tex

@@ -2,8 +2,8 @@
 \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 rand var in def, and s. \ref{sec:error-propagation}
+Probabilistic M.M: use random variable in definition, and s. \ref{sec:error-propagation}
 
-\bi{Motor encoders} Typ. 64-2048 incrm. per rev; Estim. rot
+\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

electives/amr/parts/02_Sensors-Actuators/03_cameras.tex

@@ -40,7 +40,7 @@ If p. in diff frame, e.g. $W$-frame then $\vec{u} = \mat{K}[\mat{R}_{CW}\; {_C}\
 (Projection) $\vec{x'} = \vec{p}({_C}\vec{t}_P) = t_z^{-1} \cdot [t_x, t_y]^\top$
 
 (Distortion model) $r^2 = x'^2 + y'^2$, $\vec{x''} = \vec{d}(\vec{x'})$\\
-$\vec{x''} = \! \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} \vec{x'}
+$\vec{x''} = \! \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}; \vec{x'}
     +
     {\scriptsize
     \begin{bmatrix}