[AMR] More notes

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2026-06-19 10:36:07 +02:00
parent 816b4b5942
commit cf41a61e6b
5 changed files with 63 additions and 57 deletions
@@ -1,20 +1,22 @@
\subsection{Probability} \subsection{Probability}
\shortdefinition[Sum rule] $P(X) = \sum P(X, Y) = \sum P(X \cap Y)$ \shortdefinition[Sum rule] $\P(X) = \sum \P(X, Y) = \sum \P(X \cap Y)$
\shortdefinition[Prod] $P(X, Y) = P(X | Y) P(Y) = P(Y | X) P(X)$ \shortdefinition[Prod] $\P(X \cap Y) = \P(X | Y) \P(Y) = \P(Y | X) P(X)$
\shorttheorem[Bayes] $\displaystyle P(Y_i | X) = \frac{P(X | Y_i) P(Y_i)}{\sum_{j = 1}^n P(X | Y_j) P(Y_j)}$ \shorttheorem[Bayes] $\displaystyle \P(Y_i | X) = \frac{\P(X | Y_i) \P(Y_i)}{\sum_{j = 1}^n \P(X | Y_j) \P(Y_j)}$
\shortdefinition[Cont. Var] Sums become integrals\\ \shortdefinition[Cont. Var] Sums become integrals\\
e.g. $\sum_{X} P(X) = 1$ becomes $\int p(x) \dx = 1$ e.g. $\sum_{X} \P(X) = 1$ becomes $\int \P(x) \dx = 1$
\shortdefinition[Indep.] $x, y$ indep. iff $p(x, y) = p(x) p(y)$ \shortdefinition[Indep.] $x, y$ indep. iff $\P(\cX \cap \cY) = \P(\cX) \P(\cY)$
\shortdefinition[Cond. Indep.] iff $p(x, y | z) = p(x|z) p(y|z)$ \shortdefinition[Cond. Indep.] iff $\P(\cX \cap \cY | \cZ) = \P(\cX | \cY) \P(\cY | \cZ)$
\shortdefinition $E[\vec{x}] = \int_{-\8}^{\8} \vec{x} p(\vec{x}) \dx \vec{x}$, also for $\vec{x} = \vec{f(x)}$ \shortdefinition $\E[\vec{x}] = \int_{-\8}^{\8} \vec{x} \P(\vec{x}) \dx \vec{x}$, also for $\vec{x} = \vec{f(x)}$
\shortdefinition $\text{Cov}[x] = E[\vec{x} \vec{x}^\top] - E[\vec{x}]E[\vec{x}]^\top = \mat{\Sigma}$ \shortdefinition $\text{Cov}[x] = \E[\vec{x} \vec{x}^\top] - \E[\vec{x}]\E[\vec{x}]^\top = \mat{\Sigma}$
\shortdefinition[Gauss. Dist.] $\vec{x} \sim \cN(\vec{\mu}, \mat{\Sigma})$ ($\vec{\mu}$ mean, $\mat{\Sigma}$ cov.),\\ \shortdefinition[Gauss. Dist.] $\vec{x} \sim \cN(\vec{\mu}, \mat{\Sigma})$ ($\vec{\mu}$ mean, $\mat{\Sigma}$ cov.),\\
PDF: $p(\vec{x}) = \frac{1}{\sqrt{(2\pi)^k |\mat{\Sigma}|}} \text{exp}\left( -\frac{1}{2}(\vec{x} - \vec{\mu})^\top \mat{\Sigma}^{-1} (\vec{x} - \vec{\mu}) \right)$ PDF: $f(\vec{x}) = \frac{1}{\sqrt{(2\pi)^k \det(\mat{\Sigma})}} \text{exp}\left( -\frac{1}{2}(\vec{x} - \vec{\mu})^\top \mat{\Sigma}^{-1} (\vec{x} - \vec{\mu}) \right)$
$\Sigma^{-1}$ for $\Sigma$ diagonal, inverse of diag els (e.g. $\sigma^{-1}$)
@@ -1,3 +1,6 @@
\subsection{Measurement models} \subsection{Measurement models}
$\vec{z} = \vec{b}_C + s\mat{M} {_S}\vec{\omega} + \vec{b} + \vec{n} + \vec{o}$: $\vec{z} = \vec{b}_C + s\mat{M} {_S}\vec{\omega} + \vec{b} + \vec{n} + \vec{o}$:
$\vec{b}_C$ const bias, $\vec{b}$ time bias, $\mat{M}$ missal., $\vec{n} \sim \cN(\vec{0}, \mat{R})$ noise, ${_S}\omega$ corr. meas., $\vec{o}$ other infl. $\vec{b}_C$ const bias, $\vec{b}$ time bias, $\mat{M}$ missal., $\vec{n} \sim \cN(\vec{0}, \mat{R})$ noise, ${_S}\omega$ corr. meas., $\vec{o}$ other infl.
\hl{Finding}: Is in $W$-frame, so may need $\mat{T}_{BW}$ or $\mat{R}_{BW}$.
Other model: $\vec{z} = \vec{h}(\vec{x}) + \vec{v}$, $\vec{h}(\vec{x})$ is pos of rob. dep. model
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@@ -8,24 +8,24 @@ $_{\color{blue}\fbox{W}}\,\vec{t}\,_{\color{red}\fbox{B}} = \, _{\color{blue}\fb
\shortdefinition[Rot. Mat.] \shortdefinition[Rot. Mat.]
{\scriptsize {\scriptsize
$\mat{R}_{z}(\psi)$ (Yaw), $\mat{R}_{z}(\psi)$ (Yaw),
$\mat{R}_y(\theta)$ (Pitch), $\mat{R}_y(\theta)$ (Pitch),
$\mat{R}_x(\varphi)$ (Roll)\\ $\mat{R}_x(\varphi)$ (Roll)\\
$\begin{bmatrix} $\begin{bmatrix}
c(\psi) & -s(\psi) & 0 \\ c(\psi) & -s(\psi) & 0 \\
s(\psi) & c(\psi) & 0 \\ s(\psi) & c(\psi) & 0 \\
0 & 0 & 1 0 & 0 & 1
\end{bmatrix}; \end{bmatrix};
\begin{bmatrix} \begin{bmatrix}
c(\theta) & 0 & s(\theta) \\ c(\theta) & 0 & s(\theta) \\
0 & 1 & 0 \\ 0 & 1 & 0 \\
-s(\theta) & 0 & c(\theta) \\ -s(\theta) & 0 & c(\theta) \\
\end{bmatrix}; \end{bmatrix};
\begin{bmatrix} \begin{bmatrix}
1 & 0 & 0 \\ 1 & 0 & 0 \\
0 & c(\varphi) & -s(\varphi) \\ 0 & c(\varphi) & -s(\varphi) \\
0 & s(\varphi) & c(\varphi) 0 & s(\varphi) & c(\varphi)
\end{bmatrix}$ \end{bmatrix}$
} }
\shortremark Application: ${_W} \vec{a} = \mat{R}_{WB} {_B} \vec{a}$ \shortremark Application: ${_W} \vec{a} = \mat{R}_{WB} {_B} \vec{a}$
@@ -36,52 +36,53 @@ $_{\color{blue}\fbox{W}}\,\vec{t}\,_{\color{red}\fbox{B}} = \, _{\color{blue}\fb
\shortdefinition[Euler Ang] (Tait-Brian) $\mat{R}_{EB} = \mat{R}_z(\psi) \cdot \mat{R}_y(\theta) \cdot \mat{R}_x(\varphi)$. \shortdefinition[Euler Ang] (Tait-Brian) $\mat{R}_{EB} = \mat{R}_z(\psi) \cdot \mat{R}_y(\theta) \cdot \mat{R}_x(\varphi)$.
$\begin{smallmatrix} $\begin{smallmatrix}
\psi = \arcsin\left( R_{21} \div \sqrt{1 - R_{31}^2} \right)\\ \psi = \arcsin\left( R_{21} \div \sqrt{1 - R_{31}^2} \right)\\
\theta = \arcsin(-R_{31})\\ \theta = \arcsin(-R_{31})\\
\varphi = \arcsin\left( R_{31} \div \sqrt{1 - R_{31}^2} \right)\\ \varphi = \arcsin\left( R_{31} \div \sqrt{1 - R_{31}^2} \right)\\
\end{smallmatrix}$ \end{smallmatrix}$
{\scriptsize {\scriptsize
$[\vec{n}]^\times = \begin{bmatrix} $[\vec{n}]^\times = \begin{bmatrix}
0 & -a_3 & a_2 \\ 0 & -a_3 & a_2 \\
a_3 & 0 & -a_1 \\ a_3 & 0 & -a_1 \\
-a_2 & a_1 & 0 -a_2 & a_1 & 0
\end{bmatrix}$ \end{bmatrix}$
} }
\shortdefinition[Rot. Vec] \shortdefinition[Angle-Axis]
$\vec{\alpha} = \alpha \vec{n}$ ($\vec{n}$ normal); Convert to rot. mat\\ $\vec{\alpha} = \alpha \vec{n}$ ($\vec{n}$ normal); Convert to rot. mat\\
$\mat{R}(\alpha, \vec{n}) = \mat{I}_3 + \sin(\alpha)[\vec{n}]^\times + (1 - \cos(\alpha))([\vec{n}]^\times)^2$ $\mat{R}(\alpha, \vec{n}) = \mat{I}_3 + \sin(\alpha)[\vec{n}]^\times + (1 - \cos(\alpha))([\vec{n}]^\times)^2$\\
To quat: $\vec{q} = [\vec{n}, \alpha]$
\shortdefinition[Quaternions] $q = q_w + q_x i + q_y j + q_z k$ with\\ \shortdefinition[Quaternions] $q = q_w + q_x i + q_y j + q_z k$ with\\
$i^2 = j^2 = k^2 = -1$, ($ij = -ji = k$, same for $jk$ and $ki$). $i^2 = j^2 = k^2 = -1$, ($ij = -ji = k$, same for $jk$ and $ki$).
$\vec{q} = \begin{bmatrix} $\vec{q} = \begin{bmatrix}
\vec{v}(\vec{q}), a(\vec{q}) \vec{v}(\vec{q}), a(\vec{q})
\end{bmatrix}^\top$, $a(\vec{q}) = q_w$, Add like $\C$ (by ``groups'') \end{bmatrix}^\top$, $a(\vec{q}) = q_w$, Add like $\C$ (by ``groups'')
\bi{Mult} {\scriptsize \bi{Mult} {\scriptsize
$\vec{q} \otimes \vec{p} = \begin{bmatrix} $\vec{q} \otimes \vec{p} = \begin{bmatrix}
a(\vec{q}) \vec{v}(\vec{p}) + a(\vec{p}) + \vec{v}(\vec{q}) \times \vec{v}(\vec{p}) \\ a(\vec{q}) \vec{v}(\vec{p}) + a(\vec{p}) + \vec{v}(\vec{q}) \times \vec{v}(\vec{p}) \\
a(\vec{q}) a(\vec{p}) - \vec{v}(\vec{q})^\top \vec{v}(\vec{p}) a(\vec{q}) a(\vec{p}) - \vec{v}(\vec{q})^\top \vec{v}(\vec{p})
\end{bmatrix}$ \end{bmatrix}$
} }
\bi{To Rot Mat} {\scriptsize \bi{To Rot Mat} {\scriptsize
$R_{AB} = \mat{I}_3 + 2a(\vec{q}_{AB}) [\vec{v}(\vec{q}_{AB})]^\times + 2([\vec{v}(\vec{q}_{AB})]^\times)^2$ $R_{AB} = \mat{I}_3 + 2a(\vec{q}_{AB}) [\vec{v}(\vec{q}_{AB})]^\times + 2([\vec{v}(\vec{q}_{AB})]^\times)^2$
} }
\shortdefinition[Transf. M] \shortdefinition[Transf. M]
{\scriptsize {\scriptsize
$\mat{T}_{AC} = \mat{T}_{AB} \mat{T}_{BC}$ R.-Handed typ; Aero: Left-H\\ $\mat{T}_{AC} = \mat{T}_{AB} \mat{T}_{BC}$ R.-Handed typ; Aero: Left-H\\
$\mat{T}_{AB} = \begin{bmatrix} $\mat{T}_{AB} = \begin{bmatrix}
\mat{R}_{AB} & {_A}\vec{t}_B \\ \mat{R}_{AB} & {_A}\vec{t}_B \\
\mat{0}_{1\times 3} & 1 \mat{0}_{1\times 3} & 1
\end{bmatrix}; \end{bmatrix};
\mat{T}_{BA} = \mat{T}_{AB}^{-1} = \mat{T}_{BA} = \mat{T}_{AB}^{-1} =
\begin{bmatrix} \begin{bmatrix}
\mat{R}_{AB}^\top & -\mat{R}_{AB}^\top {_A}\vec{t}_B \\ \mat{R}_{AB}^\top & -\mat{R}_{AB}^\top {_A}\vec{t}_B \\
\mat{0}_{1 \times 3} & 1 \mat{0}_{1 \times 3} & 1
\end{bmatrix}$ \end{bmatrix}$
} }
@@ -1,5 +1,5 @@
\subsection{Forward Kinematics (FK)} \subsection{Forward Kinematics (FK)}
$\mat{T}_{WB_n}(\theta) = \mat{T}_{WB_0} \mat{T}_{B_0B_1}(\theta_1) \cdots \mat{T}_{B_{n - 1}B_n}(\theta_n)$.\\ $\mat{T}_{WB_n}(\vec{\theta}) = \mat{T}_{WB_0} \mat{T}_{B_0B_1}(\theta_1) \cdots \mat{T}_{B_{n - 1}B_n}(\theta_n)$.\\
For 2R system: For 2R system:
${_W}\vec{t}_{WE} =$ ${_W}\vec{t}_{WE} =$
{\scriptsize {\scriptsize