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Moved docs to gtsam
2012-06-05 06:57:01 +08:00
#LyX 2.0 created this file. For more info see http://www.lyx.org/
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\begin_body
\begin_layout Title
Derivatives and Differentials
\end_layout
\begin_layout Author
Frank Dellaert
\end_layout
\begin_layout Standard
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\newcommand{\SqrMah}[3]{\Vert{#1}-{#2}\Vert_{#3}^{2}}
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\begin_inset FormulaMacro
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{\Vert{#1}\Vert_{#2}^{2}}
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\newcommand{\Man}{\mathcal{M}}
{\mathcal{M}}
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\begin_inset Note Comment
status open
\begin_layout Plain Layout
Derivatives
\end_layout
\end_inset
\end_layout
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\begin_inset FormulaMacro
\newcommand{\deriv}[2]{\frac{\partial#1}{\partial#2}}
{\frac{\partial#1}{\partial#2}}
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{#1\biggr\rvert_{#2}}
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\begin_inset Note Comment
status open
\begin_layout Plain Layout
Lie Groups
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
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\newcommand{\xhat}{\hat{x}}
{\hat{x}}
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\begin_inset FormulaMacro
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{\mathbb{R}^{n}}
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\begin_inset Note Comment
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SO(2)
\end_layout
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\newcommand{\Rtwo}{\mathbb{R}^{2}}
{\mathbb{R}^{2}}
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{\hat{\theta}}
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{[#1]_{+}}
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status open
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SE(2)
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\newcommand{\SEtwo}{SE(2)}
{SE(2)}
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\begin_inset FormulaMacro
\newcommand{\setwo}{\mathfrak{se(2)}}
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\begin_inset Note Comment
status open
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SO(3)
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset FormulaMacro
\newcommand{\Rthree}{\mathbb{R}^{3}}
{\mathbb{R}^{3}}
\end_inset
\begin_inset FormulaMacro
\newcommand{\SOthree}{SO(3)}
{SO(3)}
\end_inset
\begin_inset FormulaMacro
\newcommand{\sothree}{\mathfrak{so(3)}}
{\mathfrak{so(3)}}
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\begin_inset FormulaMacro
\newcommand{\what}{\hat{\omega}}
{\hat{\omega}}
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\begin_inset FormulaMacro
\newcommand{\Skew}[1]{[#1]_{\times}}
{[#1]_{\times}}
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\end_layout
\begin_layout Plain Layout
\begin_inset Note Comment
status open
\begin_layout Plain Layout
SE(3)
\end_layout
\end_inset
\end_layout
\begin_layout Plain Layout
\begin_inset FormulaMacro
\newcommand{\Rsix}{\mathbb{R}^{6}}
{\mathbb{R}^{6}}
\end_inset
\begin_inset FormulaMacro
\newcommand{\SEthree}{SE(3)}
{SE(3)}
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\begin_inset FormulaMacro
\newcommand{\sethree}{\mathfrak{se(3)}}
{\mathfrak{se(3)}}
\end_inset
\begin_inset FormulaMacro
\newcommand{\xihat}{\hat{\xi}}
{\hat{\xi}}
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Part
Theory
\end_layout
\begin_layout Section
Optimization
\end_layout
\begin_layout Standard
We will be concerned with minimizing a non-linear least squares objective
of the form
\begin_inset Formula
\begin{equation}
x^{*}=\arg\min_{x}\SqrMah{h(x)}z{\Sigma}\label{eq:objective}
\end{equation}
\end_inset
where
\begin_inset Formula $x\in\Man$
\end_inset
is a point on an
\begin_inset Formula $n$
\end_inset
-dimensional manifold (which could be
\begin_inset Formula $\Reals n$
\end_inset
, an n-dimensional Lie group
\begin_inset Formula $G$
\end_inset
, or a general manifold
\begin_inset Formula $\Man)$
\end_inset
,
\begin_inset Formula $z\in\Reals m$
\end_inset
is an observed measurement,
\begin_inset Formula $h:\Man\rightarrow\Reals m$
\end_inset
is a measurement function that predicts
\begin_inset Formula $z$
\end_inset
from
\begin_inset Formula $x$
\end_inset
, and
\begin_inset Formula $\SqrZMah e{\Sigma}\define e^{T}\Sigma^{-1}e$
\end_inset
is the squared Mahalanobis distance with covariance
\begin_inset Formula $\Sigma$
\end_inset
.
\end_layout
\begin_layout Standard
To minimize
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:objective"
\end_inset
we need a notion of how the non-linear measurement function
\begin_inset Formula $h(x)$
\end_inset
behaves in the neighborhood of a linearization point
\begin_inset Formula $a$
\end_inset
.
Loosely speaking, we would like to define an
\begin_inset Formula $m\times n$
\end_inset
Jacobian matrix
\begin_inset Formula $H_{a}$
\end_inset
such that
\begin_inset Formula
\begin{equation}
h(a\oplus\xi)\approx h(a)+H_{a}\xi\label{eq:LocalBehavior}
\end{equation}
\end_inset
with
\begin_inset Formula $\xi\in\Reals n$
\end_inset
, and the operation
\begin_inset Formula $\oplus$
\end_inset
\begin_inset Quotes eld
\end_inset
increments
\begin_inset Quotes erd
\end_inset
\begin_inset Formula $a\in\Man$
\end_inset
.
Below we more formally develop this notion, first for functions from
\begin_inset Formula $\Multi nm$
\end_inset
, then for Lie groups, and finally for manifolds.
\end_layout
\begin_layout Standard
Once equipped with the approximation
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:LocalBehavior"
\end_inset
, we can minimize the objective function
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:objective"
\end_inset
with respect to
\begin_inset Formula $\delta x$
\end_inset
instead:
\begin_inset Formula
\begin{equation}
\xi^{*}=\arg\min_{\xi}\SqrMah{h(a)+H_{a}\xi}z{\Sigma}\label{eq:ApproximateObjective}
\end{equation}
\end_inset
This can be done by setting the derivative of
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:ApproximateObjective"
\end_inset
to zero,
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
\begin_inset Formula
\[
\frac{1}{2}H_{a}^{T}(h(a)+H_{a}\xi-z)=0
\]
\end_inset
\end_layout
\end_inset
yielding the
\series bold
normal equations
\series default
,
\begin_inset Formula
\[
H_{a}^{T}H_{a}\xi=H_{a}^{T}\left(z-h(a)\right)
\]
\end_inset
which can be solved using Cholesky factorization.
Of course, we might have to iterate this multiple times, and use a trust-region
method to bound
\begin_inset Formula $\xi$
\end_inset
when the approximation
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:LocalBehavior"
\end_inset
is not good.
\end_layout
\begin_layout Section
Multivariate Differentiation
\end_layout
\begin_layout Subsection
Derivatives
\end_layout
\begin_layout Standard
For a vector space
\begin_inset Formula $\Reals n$
\end_inset
, the notion of an increment is just done by vector addition
\begin_inset Formula
\[
a\oplus\xi\define a+\xi
\]
\end_inset
and for the approximation
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:LocalBehavior"
\end_inset
we will use a Taylor expansion using multivariate differentiation.
However, loosely following
\begin_inset CommandInset citation
LatexCommand cite
key "Spivak65book"
\end_inset
, we use a perhaps unfamiliar way to define derivatives:
\end_layout
\begin_layout Definition
\begin_inset CommandInset label
LatexCommand label
name "def:differentiable"
\end_inset
We define a function
\begin_inset Formula $f:\Multi nm$
\end_inset
to be
\series bold
differentiable
\series default
at
\begin_inset Formula $a$
\end_inset
if there exists a matrix
\begin_inset Formula $f'(a)\in\Reals{m\times n}$
\end_inset
such that
\begin_inset Formula
\[
\lim_{\delta x\rightarrow0}\frac{\left|f(a)+f'(a)\xi-f(a+\xi)\right|}{\left|\xi\right|}=0
\]
\end_inset
where
\begin_inset Formula $\left|e\right|\define\sqrt{e^{T}e}$
\end_inset
is the usual norm.
If
\begin_inset Formula $f$
\end_inset
is differentiable, then the matrix
\begin_inset Formula $f'(a)$
\end_inset
is called the
\series bold
Jacobian matrix
\series default
of
\begin_inset Formula $f$
\end_inset
at
\begin_inset Formula $a$
\end_inset
, and the linear map
\begin_inset Formula $Df_{a}:\xi\mapsto f'(a)\xi$
\end_inset
is called the
\series bold
derivative
\series default
of
\begin_inset Formula $f$
\end_inset
at
\begin_inset Formula $a$
\end_inset
.
When no confusion is likely, we use the notation
\begin_inset Formula $F_{a}\define f'(a)$
\end_inset
to stress that
\begin_inset Formula $f'(a)$
\end_inset
is a matrix.
\end_layout
\begin_layout Standard
The benefit of using this definition is that it generalizes the notion of
a scalar derivative
\begin_inset Formula $f'(a):\Rone\rightarrow\Rone$
\end_inset
to multivariate functions from
\begin_inset Formula $\Multi nm$
\end_inset
.
In particular, the derivative
\begin_inset Formula $Df_{a}$
\end_inset
maps vector increments
\begin_inset Formula $\xi$
\end_inset
on
\begin_inset Formula $a$
\end_inset
to increments
\begin_inset Formula $f'(a)\xi$
\end_inset
on
\begin_inset Formula $f(a)$
\end_inset
, such that this linear map locally approximates
\begin_inset Formula $f$
\end_inset
:
\begin_inset Formula
\[
f(a+\xi)\approx f(a)+f'(a)\xi
\]
\end_inset
\end_layout
\begin_layout Example
\begin_inset CommandInset label
LatexCommand label
name "ex:projection"
\end_inset
The function
\begin_inset Formula $\pi:(x,y,z)\mapsto(x/z,y/z)$
\end_inset
projects a 3D point
\begin_inset Formula $(x,y,z)$
\end_inset
to the image plane, and has the Jacobian matrix
\begin_inset Formula
\[
\pi'(x,y,z)=\frac{1}{z}\left[\begin{array}{ccc}
1 & 0 & -x/z\\
0 & 1 & -y/z
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Subsection
Properties of Derivatives
\end_layout
\begin_layout Standard
This notion of a multivariate derivative obeys the usual rules:
\end_layout
\begin_layout Theorem
(Chain rule) If
\begin_inset Formula $f:\Multi np$
\end_inset
is differentiable at
\begin_inset Formula $a$
\end_inset
and
\begin_inset Formula $g:\Multi pm$
\end_inset
is differentiable at
\begin_inset Formula $f(a)$
\end_inset
,
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none
then
\begin_inset Formula $D(g\circ f)_{a}=Dg_{f(a)}\circ Df_{a}$
\end_inset
and
\end_layout
\end_inset
then the Jacobian matrix
\begin_inset Formula $H_{a}$
\end_inset
of
\begin_inset Formula $h=g\circ f$
\end_inset
at
\begin_inset Formula $a$
\end_inset
is the
\begin_inset Formula $m\times n$
\end_inset
matrix product
\begin_inset Formula
\[
H_{a}=G_{f(a)}F_{a}
\]
\end_inset
\end_layout
\begin_layout Proof
See
\begin_inset CommandInset citation
LatexCommand cite
key "Spivak65book"
\end_inset
\end_layout
\begin_layout Example
\begin_inset CommandInset label
LatexCommand label
name "ex:chain-rule"
\end_inset
If we follow the projection
\begin_inset Formula $\pi$
\end_inset
by a calibration step
\begin_inset Formula $\gamma:(x,y)\mapsto(u_{0}+fx,u_{0}+fy)$
\end_inset
, with
\begin_inset Formula
\[
\gamma'(x,y)=\left[\begin{array}{cc}
f & 0\\
0 & f
\end{array}\right]
\]
\end_inset
then the combined function
\begin_inset Formula $\gamma\circ\pi$
\end_inset
has the Jacobian matrix
\begin_inset Formula
\[
(\gamma\circ\pi)'(x,y)=\frac{f}{z}\left[\begin{array}{ccc}
1 & 0 & -x/z\\
0 & 1 & -y/z
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Theorem
(Inverse) If
\begin_inset Formula $f:\Multi nn$
\end_inset
is differentiable and has a differentiable inverse
\begin_inset Formula $g\define f^{-1}$
\end_inset
, then its Jacobian matrix
\begin_inset Formula $G_{a}$
\end_inset
at
\begin_inset Formula $a$
\end_inset
is just the inverse of that of
\begin_inset Formula $f$
\end_inset
, evaluated at
\begin_inset Formula $g(a)$
\end_inset
:
\begin_inset Formula
\[
G_{a}=\left[F_{g(a)}\right]^{-1}
\]
\end_inset
\end_layout
\begin_layout Proof
See
\begin_inset CommandInset citation
LatexCommand cite
key "Spivak65book"
\end_inset
\end_layout
\begin_layout Example
\begin_inset CommandInset label
LatexCommand label
name "ex:inverse"
\end_inset
The function
\begin_inset Formula $f:(x,y)\mapsto(x^{2},xy)$
\end_inset
has the Jacobian matrix
\end_layout
\begin_layout Example
\begin_inset Formula
\[
F_{(x,y)}=\left[\begin{array}{cc}
2x & 0\\
y & x
\end{array}\right]
\]
\end_inset
and, for
\begin_inset Formula $x\geq0$
\end_inset
, its inverse is the function
\begin_inset Formula $g:(x,y)\mapsto(x^{1/2},x^{-1/2}y)$
\end_inset
with the Jacobian matrix
\begin_inset Formula
\[
G_{(x,y)}=\frac{1}{2}\left[\begin{array}{cc}
x^{-1/2} & 0\\
-x^{-3/2}y & 2x^{-1/2}
\end{array}\right]
\]
\end_inset
It is easily verified that
\begin_inset Formula
\[
g'(a,b)f'(a^{1/2},a^{-1/2}b)=\frac{1}{2}\left[\begin{array}{cc}
a^{-1/2} & 0\\
-a^{-3/2}b & 2a^{-1/2}
\end{array}\right]\left[\begin{array}{cc}
2a^{1/2} & 0\\
a^{-1/2}b & a^{1/2}
\end{array}\right]=\left[\begin{array}{cc}
1 & 0\\
0 & 1
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Problem
Verify the above for
\begin_inset Formula $(a,b)=(4,6)$
\end_inset
.
Sketch the situation graphically to get insight.
\end_layout
\begin_layout Subsection
Computing Multivariate Derivatives
\end_layout
\begin_layout Standard
Computing derivatives is made easy by defining the concept of a partial
derivative:
\end_layout
\begin_layout Definition
For
\begin_inset Formula $f:\OneD n$
\end_inset
, the
\series bold
partial derivative
\series default
of
\begin_inset Formula $f$
\end_inset
at
\begin_inset Formula $a$
\end_inset
,
\series bold
\series default
\begin_inset Formula
\[
D_{j}f(a)\define\lim_{h\rightarrow0}\frac{f\left(a^{1},\ldots,a^{j}+h,\ldots,a^{n}\right)-f\left(a^{1},\ldots,a^{n}\right)}{h}
\]
\end_inset
which is the ordinary derivative of the scalar function
\begin_inset Formula $g(x)\define f\left(a^{1},\ldots,x,\ldots,a^{n}\right)$
\end_inset
.
\end_layout
\begin_layout Standard
Using this definition, one can show that the Jacobian matrix
\begin_inset Formula $F_{a}$
\end_inset
of a differentiable
\emph on
multivariate
\emph default
function
\begin_inset Formula $f:\Multi nm$
\end_inset
consists simply of the
\begin_inset Formula $m\times n$
\end_inset
partial derivatives
\begin_inset Formula $D_{j}f^{i}(a)$
\end_inset
, evaluated at
\begin_inset Formula $a\in\Reals n$
\end_inset
:
\begin_inset Formula
\[
F_{a}=\left[\begin{array}{ccc}
D_{1}f^{1}(a) & \cdots & D_{n}f^{1}(a)\\
\vdots & \ddots & \vdots\\
D_{1}f^{m}(a) & \ldots & D_{n}f^{m}(a)
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Problem
Verify the derivatives in Examples
\begin_inset CommandInset ref
LatexCommand ref
reference "ex:projection"
\end_inset
to
\begin_inset CommandInset ref
LatexCommand ref
reference "ex:inverse"
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset
\end_layout
\begin_layout Section
Multivariate Functions on Lie Groups
\end_layout
\begin_layout Subsection
Lie Groups
\end_layout
\begin_layout Standard
Lie groups are not as easy to treat as the vector space
\begin_inset Formula $\Reals n$
\end_inset
but nevertheless have a lot of structure.
To generalize the concept of the total derivative above we just need to
replace
\begin_inset Formula $a\oplus\xi$
\end_inset
in
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:ApproximateObjective"
\end_inset
with a suitable operation in the Lie group
\begin_inset Formula $G$
\end_inset
.
In particular, the notion of an exponential map allows us to define an
incremental transformation as tracing out a geodesic curve on the group
manifold along a certain
\series bold
tangent vector
\series default
\begin_inset Formula $\xi$
\end_inset
,
\begin_inset Formula
\[
a\oplus\xi\define a\exp\left(\hat{\xi}\right)
\]
\end_inset
with
\begin_inset Formula $\xi\in\Reals n$
\end_inset
for an
\begin_inset Formula $n$
\end_inset
-dimensional Lie group,
\begin_inset Formula $\hat{\xi}\in\mathfrak{g}$
\end_inset
the Lie algebra element corresponding to the vector
\begin_inset Formula $\xi$
\end_inset
, and
\begin_inset Formula $\exp\hat{\xi}$
\end_inset
the exponential map.
Note that if
\begin_inset Formula $G$
\end_inset
is equal to
\begin_inset Formula $\Reals n$
\end_inset
then composing with the exponential map
\begin_inset Formula $ae^{\xihat}$
\end_inset
is just vector addition
\begin_inset Formula $a+\xi$
\end_inset
.
\end_layout
\begin_layout Example
For the Lie group
\begin_inset Formula $\SOthree$
\end_inset
of 3D rotations the vector
\begin_inset Formula $\xi$
\end_inset
is denoted as
\begin_inset Formula $\omega$
\end_inset
and represents an angular displacement.
The Lie algebra element
\begin_inset Formula $\xihat$
\end_inset
is a skew symmetric matrix denoted as
\begin_inset Formula $\Skew{\omega}\in\sothree$
\end_inset
, and is given by
\begin_inset Formula
\[
\Skew{\omega}=\left[\begin{array}{ccc}
0 & -\omega_{z} & \omega_{y}\\
\omega_{z} & 0 & -\omega_{x}\\
-\omega_{y} & \omega_{x} & 0
\end{array}\right]
\]
\end_inset
Finally, the increment
\begin_inset Formula $a\oplus\xi=ae^{\xihat}$
\end_inset
corresponds to an incremental rotation
\begin_inset Formula $R\oplus\omega=Re^{\Skew{\omega}}$
\end_inset
.
\end_layout
\begin_layout Subsection
Derivatives
\end_layout
\begin_layout Standard
We can generalize Definition
\begin_inset CommandInset ref
LatexCommand ref
reference "def:differentiable"
\end_inset
to map exponential coordinates
\begin_inset Formula $\xi$
\end_inset
to increments
\begin_inset Formula $f'(a)\xi$
\end_inset
on
\begin_inset Formula $f(a)$
\end_inset
, such that the linear map
\begin_inset Formula $Df_{a}$
\end_inset
locally approximates a function
\begin_inset Formula $f$
\end_inset
from
\begin_inset Formula $G$
\end_inset
to
\begin_inset Formula $\Reals m$
\end_inset
:
\begin_inset Formula
\[
f(ae^{\xihat})\approx f(a)+f'(a)\xi
\]
\end_inset
\end_layout
\begin_layout Definition
We define a function
\begin_inset Formula $f:G\rightarrow\Reals m$
\end_inset
to be
\series bold
differentiable
\series default
at
\begin_inset Formula $a\in G$
\end_inset
if there exists a matrix
\begin_inset Formula $f'(a)\in\Reals{m\times n}$
\end_inset
such that
\begin_inset Formula
\[
\lim_{\xi\rightarrow0}\frac{\left|f(a)+f'(a)\xi-f(ae^{\hat{\xi}})\right|}{\left|\xi\right|}=0
\]
\end_inset
If
\begin_inset Formula $f$
\end_inset
is differentiable, then the matrix
\begin_inset Formula $f'(a)$
\end_inset
is called the
\series bold
Jacobian matrix
\series default
of
\begin_inset Formula $f$
\end_inset
at
\begin_inset Formula $a$
\end_inset
, and the linear map
\begin_inset Formula $Df_{a}:\xi\mapsto f'(a)\xi$
\end_inset
is called the
\series bold
derivative
\series default
of
\begin_inset Formula $f$
\end_inset
at
\begin_inset Formula $a$
\end_inset
.
\end_layout
\begin_layout Standard
Note that the vectors
\begin_inset Formula $\xi$
\end_inset
can be viewed as lying in the tangent space to
\begin_inset Formula $G$
\end_inset
at
\begin_inset Formula $a$
\end_inset
, but defining this rigorously would take us on a longer tour of differential
geometry.
Informally,
\begin_inset Formula $\xi$
\end_inset
is simply the direction, in a local coordinate frame, that is locally tangent
at
\begin_inset Formula $a$
\end_inset
to a geodesic curve
\begin_inset Formula $\gamma:t\mapsto ae^{\widehat{t\xi}}$
\end_inset
traced out by the exponential map, with
\begin_inset Formula $\gamma(0)=a$
\end_inset
.
\end_layout
\begin_layout Subsection
Derivative of an Action
\begin_inset CommandInset label
LatexCommand label
name "sec:Derivatives-of-Actions"
\end_inset
\end_layout
\begin_layout Standard
The (usual) action of an
\begin_inset Formula $n$
\end_inset
-dimensional matrix group
\begin_inset Formula $G$
\end_inset
is matrix-vector multiplication on
\begin_inset Formula $\mathbb{R}^{n}$
\end_inset
, i.e.,
\begin_inset Formula $f:G\times\Reals n\rightarrow\Reals n$
\end_inset
with
\begin_inset Formula
\[
f(T,p)=Tp
\]
\end_inset
Since this is a function defined on the product
\begin_inset Formula $G\times\Reals n$
\end_inset
the derivative is a linear transformation
\begin_inset Formula $Df:\Multi{2n}n$
\end_inset
with
\begin_inset Formula
\[
Df_{(T,p)}\left(\xi,\delta p\right)=D_{1}f_{(T,p)}\left(\xi\right)+D_{2}f_{(T,p)}\left(\delta p\right)
\]
\end_inset
\end_layout
\begin_layout Theorem
\begin_inset CommandInset label
LatexCommand label
name "th:Action"
\end_inset
The Jacobian matrix of the group action
\begin_inset Formula $f(T,P)=Tp$
\end_inset
at
\begin_inset Formula $(T,p)$
\end_inset
is given by
\begin_inset Formula
\[
F_{(T,p)}=\left[\begin{array}{cc}
TH(p) & T\end{array}\right]=T\left[\begin{array}{cc}
H(p) & I_{n}\end{array}\right]
\]
\end_inset
with
\begin_inset Formula $H:\Reals n\rightarrow\Reals{n\times n}$
\end_inset
a linear mapping that depends on
\begin_inset Formula $p$
\end_inset
, and
\begin_inset Formula $I_{n}$
\end_inset
the
\begin_inset Formula $n\times n$
\end_inset
identity matrix.
\end_layout
\begin_layout Proof
First, the derivative
\begin_inset Formula $D_{2}f$
\end_inset
with respect to in
\begin_inset Formula $p$
\end_inset
is easy, as its matrix is simply T:
\begin_inset Formula
\[
f(T,p+\delta p)=T(p+\delta p)=Tp+T\delta p=f(T,p)+D_{2}f(\delta p)
\]
\end_inset
For the derivative
\begin_inset Formula $D_{1}f$
\end_inset
with respect to a change in the first argument
\begin_inset Formula $T$
\end_inset
, we want
\end_layout
\begin_layout Proof
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none
\begin_inset Formula
\[
f(Te^{\hat{\xi}},p)=Te^{\hat{\xi}}p\approx Tp+D_{1}f(\xi)
\]
\end_inset
\family default
\series default
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit
Since the matrix exponential is given by the series
\begin_inset Formula $e^{A}=I+A+\frac{A^{2}}{2!}+\frac{A^{3}}{3!}+\ldots$
\end_inset
we have, to first order
\begin_inset Formula
\[
Te^{\hat{\xi}}p\approx T(I+\hat{\xi})p=Tp+T\hat{\xi}p
\]
\end_inset
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Note also that
\begin_inset Formula
\[
T\hat{\xi}p=\left(T\hat{\xi}T^{-1}\right)Tp=\left(\Ad T\xihat\right)\left(Tp\right)
\]
\end_inset
\end_layout
\end_inset
Hence, we need to show that
\begin_inset Formula
\begin{equation}
\xihat p=H(p)\xi\label{eq:Hp}
\end{equation}
\end_inset
with
\begin_inset Formula $H(p)$
\end_inset
an
\begin_inset Formula $n\times n$
\end_inset
matrix that depends on
\begin_inset Formula $p$
\end_inset
.
Expressing the map
\begin_inset Formula $\xi\rightarrow\hat{\xi}$
\end_inset
in terms of the Lie algebra generators
\begin_inset Formula $G^{i}$
\end_inset
, using tensors and Einstein summation, we have
\begin_inset Formula $\hat{\xi}_{j}^{i}=G_{jk}^{i}\xi^{k}$
\end_inset
allowing us to calculate
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\noun off
\color none
\begin_inset Formula $\hat{\xi}p$
\end_inset
\family default
\series default
\shape default
\size default
\emph default
\bar default
\noun default
\color inherit
as
\begin_inset Formula
\[
\left(\hat{\xi}p\right)^{i}=\hat{\xi}_{j}^{i}p^{j}=G_{jk}^{i}\xi^{k}p^{j}=\left(G_{jk}^{i}p^{j}\right)\xi^{k}=H_{k}^{i}(p)\xi^{k}
\]
\end_inset
\end_layout
\begin_layout Example
For 3D rotations
\begin_inset Formula $R\in\SOthree$
\end_inset
, we have
\begin_inset Formula $\hat{\omega}=\Skew{\omega}$
\end_inset
and
\begin_inset Formula
\[
G_{k=1}:\left(\begin{array}{ccc}
0 & 0 & 0\\
0 & 0 & -1\\
0 & 1 & 0
\end{array}\right)\mbox{}G_{k=2}:\left(\begin{array}{ccc}
0 & 0 & 1\\
0 & 0 & 0\\
-1 & 0 & 0
\end{array}\right)\mbox{ }G_{k=3}:\left(\begin{array}{ccc}
0 & -1 & 0\\
1 & 0 & 0\\
0 & 0 & 0
\end{array}\right)
\]
\end_inset
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\noun off
\color none
The matrices
\begin_inset Formula $\left(G_{k}^{i}\right)_{j}$
\end_inset
are obtained by assembling the
\begin_inset Formula $j^{th}$
\end_inset
columns of the generators above, yielding
\begin_inset Formula $H(p)$
\end_inset
equal to:
\begin_inset Formula
\[
\left(\begin{array}{ccc}
0 & 0 & 0\\
0 & 0 & 1\\
0 & -1 & 0
\end{array}\right)p^{1}+\left(\begin{array}{ccc}
0 & 0 & -1\\
0 & 0 & 0\\
1 & 0 & 0
\end{array}\right)p^{2}+\left(\begin{array}{ccc}
0 & 1 & 0\\
-1 & 0 & 0\\
0 & 0 & 0
\end{array}\right)p^{3}=\left(\begin{array}{ccc}
0 & p^{3} & -p^{2}\\
-p^{3} & 0 & p^{1}\\
p^{2} & -p^{1} & 0
\end{array}\right)=\Skew{-p}
\]
\end_inset
\family default
\series default
\shape default
\size default
\emph default
\bar default
\noun default
\color inherit
Hence, the Jacobian matrix of
\begin_inset Formula $f(R,p)=Rp$
\end_inset
is given by
\begin_inset Formula
\[
F_{(R,p)}=R\left(\begin{array}{cc}
\Skew{-p} & I_{3}\end{array}\right)
\]
\end_inset
\end_layout
\begin_layout Subsection
Derivative of an Inverse Action
\end_layout
\begin_layout Standard
Applying the action by the inverse of
\begin_inset Formula $T\in G$
\end_inset
yields a function
\begin_inset Formula $g:G\times\Reals n\rightarrow\Reals n$
\end_inset
defined by
\begin_inset Formula
\[
g(T,p)=T^{-1}p
\]
\end_inset
\end_layout
\begin_layout Theorem
\begin_inset CommandInset label
LatexCommand label
name "Th:InverseAction"
\end_inset
The Jacobian matrix of the inverse group action
\begin_inset Formula $g(T,p)=T^{-1}p$
\end_inset
is given by
\begin_inset Formula
\[
G_{(T,p)}=\left[\begin{array}{cc}
-H(T^{-1}p) & T^{-1}\end{array}\right]
\]
\end_inset
where
\begin_inset Formula $H:\Reals n\rightarrow\Reals{n\times n}$
\end_inset
is the same mapping as before.
\end_layout
\begin_layout Proof
Again, the derivative
\begin_inset Formula $D_{2}g$
\end_inset
with respect to in
\begin_inset Formula $p$
\end_inset
is easy, the matrix of which is simply
\begin_inset Formula $T^{-1}$
\end_inset
:
\begin_inset Formula
\[
g(T,p+\delta p)=T^{-1}(p+\delta p)=T^{-1}p+T^{-1}\delta p=g(T,p)+D_{2}g(\delta p)
\]
\end_inset
Conversely, a change in
\begin_inset Formula $T$
\end_inset
yields
\begin_inset Formula
\[
g(Te^{\xihat},p)=\left(Te^{\xihat}\right)^{-1}p=e^{-\xihat}T^{-1}p
\]
\end_inset
Similar to before, if we expand the matrix exponential we get
\begin_inset Formula
\[
e^{-A}=I-A+\frac{A^{2}}{2!}-\frac{A^{3}}{3!}+\ldots
\]
\end_inset
so
\begin_inset Formula
\[
e^{-\xihat}T^{-1}p\approx(I-\xihat)T^{-1}p=g(T,p)-\xihat\left(T^{-1}p\right)
\]
\end_inset
\end_layout
\begin_layout Example
For 3D rotations
\begin_inset Formula $R\in\SOthree$
\end_inset
we have
\begin_inset Formula $R^{-1}=R^{T}$
\end_inset
,
\begin_inset Formula $H(p)=-\Skew p$
\end_inset
, and hence the Jacobian matrix of
\begin_inset Formula $g(R,p)=R^{T}p$
\end_inset
is given by
\begin_inset Formula
\[
G_{(R,p)}=\left(\begin{array}{cc}
\Skew{R^{T}p} & R^{T}\end{array}\right)
\]
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
My earlier attempt: because the wedge operator is linear, we have
\end_layout
\begin_layout Plain Layout
\begin_inset Formula
\begin{eqnarray*}
f(\xi+x) & = & \exp\widehat{\left(\xi+x\right)}\\
& = & \exp\left(\xihat+\hat{x}\right)
\end{eqnarray*}
\end_inset
However, except for commutative Lie groups, it is not true that
\begin_inset Formula $\exp\left(\xihat+\hat{x}\right)=\exp\xihat\exp\hat{x}$
\end_inset
.
However, if we expand the matrix exponential to second order and assume
\begin_inset Formula $x\rightarrow0$
\end_inset
we do have
\begin_inset Formula
\[
\exp\left(\xihat+\hat{x}\right)\approx I+\xihat+\hat{x}+\frac{1}{2}\xihat^{2}+\xhat\xihat
\]
\end_inset
Now, if we ask what
\begin_inset Formula $\hat{y}$
\end_inset
would effect the same change:
\begin_inset Formula
\begin{eqnarray*}
\exp\xihat\exp\yhat & = & I+\xihat+\hat{x}+\frac{1}{2}\xihat^{2}+\xhat\xihat\\
\exp\xihat(I+\yhat) & = & I+\xihat+\hat{x}+\frac{1}{2}\xihat^{2}+\xhat\xihat\\
\left(\exp\xihat\right)\yhat & = & \xhat+\xhat\xihat
\end{eqnarray*}
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Section
Instantaneous Velocity
\end_layout
\begin_layout Standard
For matrix Lie groups, if we have a matrix
\begin_inset Formula $T_{b}^{n}(t)$
\end_inset
that depends on a parameter
\begin_inset Formula $t$
\end_inset
, i.e.,
\begin_inset Formula $T_{b}^{n}(t)$
\end_inset
follows a curve on the manifold, then it would be of interest to find the
velocity of a point
\begin_inset Formula $q^{n}(t)=T_{b}^{n}(t)p^{b}$
\end_inset
acted upon by
\begin_inset Formula $T_{b}^{n}(t)$
\end_inset
.
We can express the velocity of
\begin_inset Formula $q(t)$
\end_inset
in both the n-frame and b-frame:
\begin_inset Formula
\[
\dot{q}^{n}=\dot{T}_{b}^{n}p^{b}=\dot{T}_{b}^{n}\left(T_{b}^{n}\right)^{-1}p^{n}\mbox{\,\,\,\,\ and\,\,\,\,}\dot{q}^{b}=\left(T_{b}^{n}\right)^{-1}\dot{q}^{n}=\left(T_{b}^{n}\right)^{-1}\dot{T}_{b}^{n}p^{b}
\]
\end_inset
Both the matrices
\begin_inset Formula $\xihat_{nb}^{n}\define\dot{T}_{b}^{n}\left(T_{b}^{n}\right)^{-1}$
\end_inset
and
\begin_inset Formula $\xihat_{nb}^{b}\define\left(T_{b}^{n}\right)^{-1}\dot{T}_{b}^{n}$
\end_inset
are skew-symmetric Lie algebra elements that describe the
\series bold
instantaneous velocity
\series default
\begin_inset CommandInset citation
LatexCommand cite
after "page 51 for rotations, page 419 for SE(3)"
key "Murray94book"
\end_inset
.
We will revisit this for both rotations and rigid 3D transformations.
\end_layout
\begin_layout Section
Differentials: Smooth Mapping between Lie Groups
\end_layout
\begin_layout Subsection
Motivation and Definition
\end_layout
\begin_layout Standard
The above shows how to compute the derivative of a function
\begin_inset Formula $f:G\rightarrow\Reals m$
\end_inset
.
However, what if the argument to
\begin_inset Formula $f$
\end_inset
is itself the result of a mapping between Lie groups? In other words,
\begin_inset Formula $f=g\circ\varphi$
\end_inset
, with
\begin_inset Formula $g:G\rightarrow\Reals m$
\end_inset
and where
\begin_inset Formula $\varphi:H\rightarrow G$
\end_inset
is a smooth mapping from the
\begin_inset Formula $n$
\end_inset
-dimensional Lie group
\begin_inset Formula $H$
\end_inset
to the
\begin_inset Formula $p$
\end_inset
-dimensional Lie group
\begin_inset Formula $G$
\end_inset
.
In this case, one would expect that we can arrive at
\begin_inset Formula $Df_{a}$
\end_inset
by composing linear maps, as follows:
\begin_inset Formula
\[
f'(a)=(g\circ\varphi)'(a)=G_{\varphi(a)}\varphi'(a)
\]
\end_inset
where
\begin_inset Formula $\varphi'(a)$
\end_inset
is an
\begin_inset Formula $n\times p$
\end_inset
matrix that is the best linear approximation to the map
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none
\begin_inset Formula $\varphi:H\rightarrow G$
\end_inset
.
The corresponding linear map
\begin_inset Formula $D\varphi_{a}$
\end_inset
is called the
\family default
\series bold
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit
differential
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none
\family default
\series default
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit
or
\series bold
pushforward
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none
of
\begin_inset Formula $ $
\end_inset
the mapping
\begin_inset Formula $\varphi$
\end_inset
at
\begin_inset Formula $a$
\end_inset
.
\end_layout
\begin_layout Standard
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none
Because a rigorous definition will lead us too far astray, here we only
informally define the pushforward of
\begin_inset Formula $\varphi$
\end_inset
at
\begin_inset Formula $a$
\end_inset
as the linear map
\begin_inset Formula $D\varphi_{a}:\Multi np$
\end_inset
such that
\begin_inset Formula $D\varphi_{a}\left(\xi\right)\define\varphi'(a)\xi$
\end_inset
and
\family default
\series default
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit
\begin_inset Formula
\begin{equation}
\varphi\left(ae^{\xihat}\right)\approx\varphi\left(a\right)\exp\left(\widehat{\varphi'(a)\xi}\right)\label{eq:pushforward}
\end{equation}
\end_inset
with equality for
\begin_inset Formula $\xi\rightarrow0$
\end_inset
.
We call
\begin_inset Formula $\varphi'(a)$
\end_inset
the
\series bold
Jacobian matrix
\series default
of the map
\begin_inset Formula $\varphi$
\end_inset
at
\begin_inset Formula $a$
\end_inset
.
Below we show that even with this informal definition we can deduce the
pushforward in a number of useful cases.
\end_layout
\begin_layout Subsection
Left Multiplication with a Constant
\end_layout
\begin_layout Theorem
Suppose
\begin_inset Formula $G$
\end_inset
is an
\begin_inset Formula $n$
\end_inset
-dimensional Lie group, and
\begin_inset Formula $\varphi:G\rightarrow G$
\end_inset
is defined as
\begin_inset Formula $\varphi(g)=hg$
\end_inset
, with
\begin_inset Formula $h\in G$
\end_inset
a constant.
Then
\begin_inset Formula $D\varphi_{a}$
\end_inset
is the identity mapping and
\begin_inset Formula
\[
\varphi'(a)=I_{n}
\]
\end_inset
\end_layout
\begin_layout Proof
Defining
\begin_inset Formula $y=D\varphi_{a}x$
\end_inset
as in
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:pushforward"
\end_inset
, we have
\begin_inset Formula
\begin{eqnarray*}
\varphi(a)e^{\yhat} & = & \varphi(ae^{\xhat})\\
hae^{\yhat} & = & hae^{\xhat}\\
y & = & x
\end{eqnarray*}
\end_inset
\end_layout
\begin_layout Subsection
Pushforward of the Inverse Mapping
\end_layout
\begin_layout Standard
A well known property of Lie groups is the the fact that applying an incremental
change
\begin_inset Formula $\xihat$
\end_inset
in a different frame
\begin_inset Formula $g$
\end_inset
can be applied in a single step by applying the change
\begin_inset Formula $Ad_{g}\xihat$
\end_inset
in the original frame,
\begin_inset Formula
\begin{equation}
ge^{\xihat}g^{-1}=\exp\left(Ad_{g}\xihat\right)\label{eq:Adjoint2}
\end{equation}
\end_inset
where
\begin_inset Formula $Ad_{g}:\mathfrak{g}\rightarrow\mathfrak{g}$
\end_inset
is the
\series bold
adjoint representation
\series default
.
This comes in handy in the following:
\end_layout
\begin_layout Theorem
Suppose that
\begin_inset Formula $\varphi:G\rightarrow G$
\end_inset
is defined as the mapping from an element
\begin_inset Formula $g$
\end_inset
to its
\series bold
inverse
\series default
\begin_inset Formula $g^{-1}$
\end_inset
, i.e.,
\begin_inset Formula $\varphi(g)=g^{-1}$
\end_inset
, then the pushforward
\begin_inset Formula $D\varphi_{a}$
\end_inset
satisfies
\begin_inset Formula
\begin{align}
\left(D\varphi_{a}x\right)\hat{} & =-Ad_{a}\xhat\label{eq:Dinverse}
\end{align}
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
noindent
\end_layout
\end_inset
In other words, and this is intuitive in hindsight, approximating the inverse
is accomplished by negation of
\begin_inset Formula $\xihat$
\end_inset
, along with an adjoint to make sure it is applied in the right frame.
\begin_inset ERT
status open
\begin_layout Plain Layout
\backslash
noindent
\end_layout
\end_inset
Note, however, that
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:Dinverse"
\end_inset
does not immediately yield a useful expression for the Jacobian matrix
\begin_inset Formula $\varphi'(a)$
\end_inset
, but in many important cases this will turn out to be easy.
\end_layout
\begin_layout Proof
Defining
\begin_inset Formula $y=D\varphi_{a}x$
\end_inset
as in
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:pushforward"
\end_inset
, we have
\begin_inset Formula
\begin{eqnarray*}
\varphi(a)e^{\yhat} & = & \varphi(ae^{\xhat})\\
a^{-1}e^{\yhat} & = & \left(ae^{\xhat}\right)^{-1}\\
e^{\yhat} & = & -ae^{\xhat}a^{-1}\\
\yhat & = & -\Ad a\xhat
\end{eqnarray*}
\end_inset
\end_layout
\begin_layout Example
For 3D rotations
\begin_inset Formula $R\in\SOthree$
\end_inset
we have
\begin_inset Formula
\[
Ad_{g}(\hat{\omega})=R\hat{\omega}R^{T}=\Skew{R\omega}
\]
\end_inset
and hence the pushforward for the inverse mapping
\begin_inset Formula $\varphi(R)=R^{T}$
\end_inset
has the matrix
\begin_inset Formula $\varphi'(R)=-R$
\end_inset
.
\end_layout
\begin_layout Subsection
Right Multiplication with a Constant
\end_layout
\begin_layout Theorem
Suppose
\begin_inset Formula $\varphi:G\rightarrow G$
\end_inset
is defined as
\begin_inset Formula $\varphi(g)=gh$
\end_inset
, with
\begin_inset Formula $h\in G$
\end_inset
a constant.
Then
\begin_inset Formula $D\varphi_{a}$
\end_inset
satisfies
\begin_inset Formula
\[
\left(D\varphi_{a}x\right)\hat{}=\Ad{h^{-1}}\xhat
\]
\end_inset
\end_layout
\begin_layout Proof
Defining
\begin_inset Formula $y=D\varphi_{a}x$
\end_inset
as in
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:pushforward"
\end_inset
, we have
\begin_inset Formula
\begin{align*}
\varphi(a)e^{\yhat} & =\varphi(ae^{\xhat})\\
ahe & =ae^{\xhat}h\\
e^{\yhat} & =h^{-1}e^{\xhat}h=\exp\left(\Ad{h^{-1}}\xhat\right)\\
\yhat & =\Ad{h^{-1}}\xhat
\end{align*}
\end_inset
\end_layout
\begin_layout Example
In the case of 3D rotations, right multiplication with a constant rotation
\begin_inset Formula $R$
\end_inset
is done through the mapping
\begin_inset Formula $\varphi(A)=AR$
\end_inset
, and satisfies
\begin_inset Formula
\[
\Skew{D\varphi_{A}x}=\Ad{R^{T}}\Skew x
\]
\end_inset
For 3D rotations
\begin_inset Formula $R\in\SOthree$
\end_inset
we have
\begin_inset Formula
\[
Ad_{R^{T}}(\hat{\omega})=R^{T}\hat{\omega}R=\Skew{R^{T}\omega}
\]
\end_inset
and hence the Jacobian matrix of
\begin_inset Formula $\varphi$
\end_inset
at
\begin_inset Formula $A$
\end_inset
is
\begin_inset Formula $\varphi'(A)=R^{T}$
\end_inset
.
\end_layout
\begin_layout Subsection
Pushforward of Compose
\end_layout
\begin_layout Theorem
If we define the mapping
\begin_inset Formula $\varphi:G\times G\rightarrow G$
\end_inset
as the product of two group elements
\begin_inset Formula $g,h\in G$
\end_inset
, i.e.,
\begin_inset Formula $\varphi(g,h)=gh$
\end_inset
, then the pushforward will satisfy
\begin_inset Formula
\[
D\varphi_{(a,b)}(x,y)=D_{1}\varphi_{(a,b)}x+D_{2}\varphi_{(a,b)}y
\]
\end_inset
with
\begin_inset Formula
\[
\left(D_{1}\varphi_{(a,b)}x\right)\hat{}=\Ad{b^{-1}}\xhat\mbox{\;\ and\;}D_{2}\varphi_{(a,b)}y=y
\]
\end_inset
\end_layout
\begin_layout Proof
Looking at the first argument, the proof is very similar to right multiplication
with a constant
\begin_inset Formula $b$
\end_inset
.
Indeed, defining
\begin_inset Formula $y=D\varphi_{a}x$
\end_inset
as in
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:pushforward"
\end_inset
, we have
\begin_inset Formula
\begin{align}
\varphi(a,b)e^{\yhat} & =\varphi(ae^{\xhat},b)\nonumber \\
abe^{\yhat} & =ae^{\xhat}b\nonumber \\
e^{\yhat} & =b^{-1}e^{\xhat}b=\exp\left(\Ad{b^{-1}}\xhat\right)\nonumber \\
\yhat & =\Ad{b^{-1}}\xhat\label{eq:Dcompose1}
\end{align}
\end_inset
In other words, to apply an incremental change
\begin_inset Formula $\xhat$
\end_inset
to
\begin_inset Formula $a$
\end_inset
we first need to undo
\begin_inset Formula $b$
\end_inset
, then apply
\begin_inset Formula $\xhat$
\end_inset
, and then apply
\begin_inset Formula $b$
\end_inset
again.
Using
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:Adjoint2"
\end_inset
this can be done in one step by simply applying
\begin_inset Formula $\Ad{b^{-1}}\xhat$
\end_inset
.
\end_layout
\begin_layout Proof
The second argument is quite a bit easier and simply yields the identity
mapping:
\begin_inset Formula
\begin{align}
\varphi(a,b)e^{\yhat} & =\varphi(a,be^{\xhat})\nonumber \\
abe^{\yhat} & =abe^{\xhat}\nonumber \\
y & =x\label{eq:Dcompose2}
\end{align}
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Note Note
status open
\begin_layout Plain Layout
In summary, the Jacobian matrix of
\begin_inset Formula $\varphi(g,h)=gh$
\end_inset
at
\begin_inset Formula $(a,b)\in G\times G$
\end_inset
is given by
\begin_inset Formula
\[
\varphi'(a,b)=?
\]
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Example
For 3D rotations
\begin_inset Formula $A,B\in\SOthree$
\end_inset
we have
\begin_inset Formula $\varphi(A,B)=AB$
\end_inset
, and
\begin_inset Formula $\Ad{B^{T}}\Skew{\omega}=\Skew{B^{T}\omega}$
\end_inset
, hence the Jacobian matrix
\begin_inset Formula $\varphi'(A,B)$
\end_inset
of composing two rotations is given by
\begin_inset Formula
\[
\varphi'(A,B)=\left[\begin{array}{cc}
B^{T} & I_{3}\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Subsection
Pushforward of Between
\end_layout
\begin_layout Standard
Finally, let us find the pushforward of
\series bold
between
\series default
, defined as
\begin_inset Formula $\varphi(g,h)=g^{-1}h$
\end_inset
.
For the first argument we reason as:
\begin_inset Formula
\begin{align}
\varphi(g,h)e^{\yhat} & =\varphi(ge^{\xhat},h)\nonumber \\
g^{-1}he^{\yhat} & =\left(ge^{\xhat}\right)^{-1}h=-e^{\xhat}g^{-1}h\nonumber \\
e^{\yhat} & =-\left(h^{-1}g\right)e^{\xhat}\left(h^{-1}g\right)^{-1}=-\exp\Ad{\left(h^{-1}g\right)}\xhat\nonumber \\
\yhat & =-\Ad{\left(h^{-1}g\right)}\xhat=-\Ad{\varphi\left(h,g\right)}\xhat\label{eq:Dbetween1}
\end{align}
\end_inset
The second argument yields the identity mapping.
\end_layout
\begin_layout Example
For 3D rotations
\begin_inset Formula $A,B\in\SOthree$
\end_inset
we have
\begin_inset Formula $\varphi(A,B)=A^{T}B$
\end_inset
, and
\begin_inset Formula $\Ad{B^{T}A}\Skew{-\omega}=\Skew{-B^{T}A\omega}$
\end_inset
, hence the Jacobian matrix
\begin_inset Formula $\varphi'(A,B)$
\end_inset
of between is given by
\begin_inset Formula
\[
\varphi'(A,B)=\left[\begin{array}{cc}
\left(-B^{T}A\right) & I_{3}\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Subsection
Numerical PushForward
\end_layout
\begin_layout Standard
Let's examine
\begin_inset Formula
\[
f\left(g\right)e^{\yhat}=f\left(ge^{\xhat}\right)
\]
\end_inset
and multiply with
\begin_inset Formula $f(g)^{-1}$
\end_inset
on both sides:
\begin_inset Formula
\[
e^{\yhat}=f\left(g\right)^{-1}f\left(ge^{\xhat}\right)
\]
\end_inset
We then take the log (which in our case returns
\begin_inset Formula $y$
\end_inset
, not
\begin_inset Formula $\yhat$
\end_inset
):
\begin_inset Formula
\[
y(x)=\log\left[f\left(g\right)^{-1}f\left(ge^{\xhat}\right)\right]
\]
\end_inset
Let us look at
\begin_inset Formula $x=0$
\end_inset
, and perturb in direction
\begin_inset Formula $i$
\end_inset
,
\begin_inset Formula $e_{i}=[0,0,1,0,0]$
\end_inset
.
Then take derivative,
\begin_inset Formula
\[
\deriv{y(d)}d\define\lim_{d\rightarrow0}\frac{y(d)-y(0)}{d}=\lim_{d\rightarrow0}\frac{1}{d}\log\left[f\left(g\right)^{-1}f\left(ge^{\widehat{de_{i}}}\right)\right]
\]
\end_inset
which is the basis for a numerical derivative scheme.
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Not understood yet: Let us also look at a chain rule.
If we know the behavior at the origin
\begin_inset Formula $I$
\end_inset
, we can extrapolate
\begin_inset Formula
\[
f(ge^{\xhat})=f(ge^{\xhat}g^{-1}g)=f(e^{\Ad g\xhat}g)
\]
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Subsection
Derivative of the Exponential and Logarithm Map
\end_layout
\begin_layout Theorem
\begin_inset CommandInset label
LatexCommand label
name "D-exp"
\end_inset
The derivative of the function
\begin_inset Formula $f:\Reals n\rightarrow G$
\end_inset
that applies the wedge operator followed by the exponential map, i.e.,
\begin_inset Formula $f(\xi)=\exp\xihat$
\end_inset
, is the identity map for
\begin_inset Formula $\xi=0$
\end_inset
.
\end_layout
\begin_layout Proof
For
\begin_inset Formula $\xi=0$
\end_inset
, we have
\begin_inset Formula
\begin{eqnarray*}
f(\xi)e^{\yhat} & = & f(\xi+x)\\
f(0)e^{\yhat} & = & f(0+x)\\
e^{\yhat} & = & e^{\xhat}
\end{eqnarray*}
\end_inset
\end_layout
\begin_layout Corollary
The derivative of the inverse
\begin_inset Formula $f^{-1}$
\end_inset
is the identity as well, i.e., for
\begin_inset Formula $T=e$
\end_inset
, the identity element in
\begin_inset Formula $G$
\end_inset
.
\end_layout
\begin_layout Standard
For
\begin_inset Formula $\xi\neq0$
\end_inset
, things are not simple, see .
\begin_inset Flex URL
status collapsed
\begin_layout Plain Layout
http://deltaepsilons.wordpress.com/2009/11/06/helgasons-formula-for-the-differenti
al-of-the-exponential/
\end_layout
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset
\end_layout
\begin_layout Section
General Manifolds
\end_layout
\begin_layout Subsection
Retractions
\end_layout
\begin_layout Standard
\begin_inset FormulaMacro
\newcommand{\retract}{\mathcal{R}}
{\mathcal{R}}
\end_inset
\end_layout
\begin_layout Standard
General manifolds that are not Lie groups do not have an exponential map,
but can still be handled by defining a
\series bold
retraction
\series default
\begin_inset Formula $\retract:\Man\times\Reals n\rightarrow\Man$
\end_inset
, such that
\begin_inset Formula
\[
a\oplus\xi\define\retract_{a}\left(\xi\right)
\]
\end_inset
A retraction
\begin_inset CommandInset citation
LatexCommand cite
key "Absil07book"
\end_inset
is required to be tangent to geodesics on the manifold
\begin_inset Formula $\Man$
\end_inset
at
\begin_inset Formula $a$
\end_inset
.
We can define many retractions for a manifold
\begin_inset Formula $\Man$
\end_inset
, even for those with more structure.
For the vector space
\begin_inset Formula $\Reals n$
\end_inset
the retraction is just vector addition, and for Lie groups the obvious
retraction is simply the exponential map, i.e.,
\begin_inset Formula $\retract_{a}(\xi)=a\cdot\exp\xihat$
\end_inset
.
However, one can choose other, possibly computationally attractive retractions,
as long as around a they agree with the geodesic induced by the exponential
map, i.e.,
\begin_inset Formula
\[
\lim_{\xi\rightarrow0}\frac{\left|a\cdot\exp\xihat-\retract_{a}\left(\xi\right)\right|}{\left|\xi\right|}=0
\]
\end_inset
\end_layout
\begin_layout Example
For
\begin_inset Formula $\SEthree$
\end_inset
, instead of using the true exponential map it is computationally more efficient
to define the retraction, which uses a first order approximation of the
translation update
\begin_inset Formula
\[
\retract_{T}\left(\left[\begin{array}{c}
\omega\\
v
\end{array}\right]\right)=\left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]\left[\begin{array}{cc}
e^{\Skew{\omega}} & v\\
0 & 1
\end{array}\right]=\left[\begin{array}{cc}
Re^{\Skew{\omega}} & t+Rv\\
0 & 1
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Subsection
Derivatives
\end_layout
\begin_layout Standard
Equipped with a retraction, then, we can generalize the notion of a derivative
for functions
\begin_inset Formula $f$
\end_inset
from general a manifold
\begin_inset Formula $\Man$
\end_inset
to
\begin_inset Formula $\Reals m$
\end_inset
:
\end_layout
\begin_layout Definition
We define a function
\begin_inset Formula $f:\Man\rightarrow\Reals m$
\end_inset
to be
\series bold
differentiable
\series default
at
\begin_inset Formula $a\in\Man$
\end_inset
if there exists a matrix
\begin_inset Formula $f'(a)$
\end_inset
such that
\begin_inset Formula
\[
\lim_{\xi\rightarrow0}\frac{\left|f(a)+f'(a)\xi-f\left(\retract_{a}(\xi)\right)\right|}{\left|\xi\right|}=0
\]
\end_inset
with
\begin_inset Formula $\xi\in\Reals n$
\end_inset
for an
\begin_inset Formula $n$
\end_inset
-dimensional manifold, and
\begin_inset Formula $\retract_{a}:\Reals n\rightarrow\Man$
\end_inset
a retraction
\begin_inset Formula $\retract$
\end_inset
at
\begin_inset Formula $a$
\end_inset
.
If
\begin_inset Formula $f$
\end_inset
is differentiable, then
\begin_inset Formula $f'(a)$
\end_inset
is called the
\series bold
Jacobian matrix
\series default
of
\begin_inset Formula $f$
\end_inset
at
\begin_inset Formula $a$
\end_inset
, and the linear transformation
\begin_inset Formula $Df_{a}:\xi\mapsto f'(a)\xi$
\end_inset
is called the
\series bold
derivative
\series default
of
\begin_inset Formula $f$
\end_inset
at
\begin_inset Formula $a$
\end_inset
.
\end_layout
\begin_layout Definition
For manifolds that are also Lie groups, the derivative of any function
\begin_inset Formula $f:G\rightarrow\Reals m$
\end_inset
will agree no matter what retraction
\begin_inset Formula $\retract$
\end_inset
is used.
\end_layout
\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset
\end_layout
\begin_layout Part
Practice
\end_layout
\begin_layout Standard
Below we apply the results derived in the theory part to the geometric objects
we use in GTSAM.
Above we preferred the modern notation
\begin_inset Formula $D_{1}f$
\end_inset
for the partial derivative.
Below (because this was written earlier) we use the more classical notation
\begin_inset Formula
\[
\deriv{f(x,y)}x
\]
\end_inset
In addition, for Lie groups we will abuse the notation and take
\begin_inset Formula
\[
\at{\deriv{\varphi(g)}{\xi}}a
\]
\end_inset
to be the Jacobian matrix
\begin_inset Formula $\varphi'($
\end_inset
a) of the mapping
\begin_inset Formula $\varphi$
\end_inset
at
\begin_inset Formula $a\in G$
\end_inset
, associated with the pushforward
\begin_inset Formula $D\varphi_{a}$
\end_inset
.
\end_layout
\begin_layout Section
SLAM Example
\end_layout
\begin_layout Standard
Let us examine a visual SLAM example.
We have 2D measurements
\begin_inset Formula $z_{ij}$
\end_inset
, where each measurement is predicted by
\begin_inset Formula
\[
z_{ij}=h(T_{i},p_{j})=\pi(T_{i}^{-1}p_{j})
\]
\end_inset
where
\begin_inset Formula $T_{i}$
\end_inset
is the 3D pose of the
\begin_inset Formula $i^{th}$
\end_inset
camera,
\begin_inset Formula $p_{j}$
\end_inset
is the location of the
\begin_inset Formula $j^{th}$
\end_inset
point, and
\begin_inset Formula $\pi:(x,y,z)\mapsto(x/z,y/z)$
\end_inset
is the camera projection function from Example
\begin_inset CommandInset ref
LatexCommand ref
reference "ex:projection"
\end_inset
.
\end_layout
\begin_layout Section
BetweenFactor
\end_layout
\begin_layout Standard
BetweenFactor is often used to summarize
\end_layout
\begin_layout Standard
Theorem
\begin_inset CommandInset ref
LatexCommand ref
reference "D-exp"
\end_inset
about the derivative of the exponential map
\begin_inset Formula $f:\xi\mapsto\exp\xihat$
\end_inset
being identity only at
\begin_inset Formula $\xi=0$
\end_inset
has implications for GTSAM.
Given two elements
\begin_inset Formula $T_{1}$
\end_inset
and
\begin_inset Formula $T_{2}$
\end_inset
, BetweenFactor evaluates
\begin_inset Formula
\[
g(T_{1},T_{2};Z)=f^{-1}\left(\mathop{between}(Z,\mathop{between}(T_{1},T_{2})\right)=f^{-1}\left(Z^{-1}\left(T_{1}^{-1}T_{2}\right)\right)
\]
\end_inset
but because it is assumed that
\begin_inset Formula $Z\approx T_{1}^{-1}T_{2}$
\end_inset
, and hence we have
\begin_inset Formula $Z^{-1}T_{1}^{-1}T_{2}\approx e$
\end_inset
and the derivative should be good there.
Note that the derivative of
\emph on
between
\emph default
is identity in its second argument.
\end_layout
\begin_layout Section
Point3
\end_layout
\begin_layout Standard
A cross product
\begin_inset Formula $a\times b$
\end_inset
can be written as a matrix multiplication
\begin_inset Formula
\[
a\times b=\Skew ab
\]
\end_inset
where
\begin_inset Formula $\Skew a$
\end_inset
is a skew-symmetric matrix defined as
\begin_inset Formula
\[
\Skew{x,y,z}=\left[\begin{array}{ccc}
0 & -z & y\\
z & 0 & -x\\
-y & x & 0
\end{array}\right]
\]
\end_inset
We also have
\begin_inset Formula
\[
a^{T}\Skew b=-(\Skew ba)^{T}=-(a\times b)^{T}
\]
\end_inset
The derivative of a cross product
\begin_inset Formula
\begin{equation}
\frac{\partial(a\times b)}{\partial a}=\Skew{-b}\label{eq:Dcross1}
\end{equation}
\end_inset
\begin_inset Formula
\begin{equation}
\frac{\partial(a\times b)}{\partial b}=\Skew a\label{eq:Dcross2}
\end{equation}
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset
\end_layout
\begin_layout Section
2D Rotations
\end_layout
\begin_layout Subsection
Rot2 in GTSAM
\end_layout
\begin_layout Standard
A rotation is stored as
\begin_inset Formula $(\cos\theta,\sin\theta)$
\end_inset
.
An incremental rotation is applied using the trigonometric sum rule:
\begin_inset Formula
\[
\cos\theta'=\cos\theta\cos\delta-\sin\theta\sin\delta
\]
\end_inset
\begin_inset Formula
\[
\sin\theta'=\sin\theta\cos\delta+\cos\theta\sin\delta
\]
\end_inset
where
\begin_inset Formula $\delta$
\end_inset
is an incremental rotation angle.
\end_layout
\begin_layout Subsection
Derivatives of Actions
\end_layout
\begin_layout Standard
In the case of
\begin_inset Formula $\SOtwo$
\end_inset
the vector space is
\begin_inset Formula $\Rtwo$
\end_inset
, and the group action
\begin_inset Formula $f(R,p)$
\end_inset
corresponds to rotating the 2D point
\begin_inset Formula $p$
\end_inset
\begin_inset Formula
\[
f(R,p)=Rp
\]
\end_inset
According to Theorem
\begin_inset CommandInset ref
LatexCommand ref
reference "th:Action"
\end_inset
, the Jacobian matrix of
\begin_inset Formula $f$
\end_inset
is given by
\begin_inset Formula
\[
f'(R,p)=\left[\begin{array}{cc}
RH(p) & R\end{array}\right]
\]
\end_inset
with
\begin_inset Formula $H:\Reals 2\rightarrow\Reals{2\times2}$
\end_inset
a linear mapping that depends on
\begin_inset Formula $p$
\end_inset
.
In the case of
\begin_inset Formula $\SOtwo$
\end_inset
, we can find
\begin_inset Formula $H(p)$
\end_inset
by equating (as in Equation
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:Hp"
\end_inset
):
\begin_inset Formula
\[
\skew wp=\left[\begin{array}{cc}
0 & -\omega\\
\omega & 0
\end{array}\right]\left[\begin{array}{c}
x\\
y
\end{array}\right]=\left[\begin{array}{c}
-y\\
x
\end{array}\right]\omega=H(p)\omega
\]
\end_inset
Note that
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none
\begin_inset Formula
\[
H(p)=\left[\begin{array}{c}
-y\\
x
\end{array}\right]=\left[\begin{array}{cc}
0 & -1\\
1 & 0
\end{array}\right]\left[\begin{array}{c}
x\\
y
\end{array}\right]=R_{\pi/2}p
\]
\end_inset
and since 2D rotations commute, we also have, with
\begin_inset Formula $q=Rp$
\end_inset
\family default
\series default
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit
:
\begin_inset Formula
\[
f'(R,p)=\left[\begin{array}{cc}
R\left(R_{\pi/2}p\right) & R\end{array}\right]=\left[\begin{array}{cc}
R_{\pi/2}q & R\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Subsection
Pushforwards of Mappings
\end_layout
\begin_layout Standard
Since
\begin_inset Formula $\Ad R\skew{\omega}=\skew{\omega}$
\end_inset
, we have the derivative of
\series bold
inverse
\series default
,
\begin_inset Formula
\[
\frac{\partial R^{T}}{\partial\omega}=-\Ad R=-1\mbox{ }
\]
\end_inset
\series bold
compose,
\series default
\begin_inset Formula
\[
\frac{\partial\left(R_{1}R_{2}\right)}{\partial\omega_{1}}=\Ad{R_{2}^{T}}=1\mbox{ and }\frac{\partial\left(R_{1}R_{2}\right)}{\partial\omega_{2}}=1
\]
\end_inset
and
\series bold
between:
\series default
\begin_inset Formula
\[
\frac{\partial\left(R_{1}^{T}R_{2}\right)}{\partial\omega_{1}}=-\Ad{R_{2}^{T}R_{1}}=-1\mbox{ and }\frac{\partial\left(R_{1}^{T}R_{2}\right)}{\partial\omega_{2}}=1
\]
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset
\end_layout
\begin_layout Section
2D Rigid Transformations
\end_layout
\begin_layout Subsection
The derivatives of Actions
\end_layout
\begin_layout Standard
The action of
\begin_inset Formula $\SEtwo$
\end_inset
on 2D points is done by embedding the points in
\begin_inset Formula $\mathbb{R}^{3}$
\end_inset
by using homogeneous coordinates
\begin_inset Formula
\[
f(T,p)=\hat{q}=\left[\begin{array}{c}
q\\
1
\end{array}\right]=\left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]\left[\begin{array}{c}
p\\
1
\end{array}\right]=T\hat{p}
\]
\end_inset
To find the derivative, we write the quantity
\begin_inset Formula $\xihat\hat{p}$
\end_inset
as the product of the
\begin_inset Formula $3\times3$
\end_inset
matrix
\begin_inset Formula $H(p)$
\end_inset
with
\begin_inset Formula $\xi$
\end_inset
:
\begin_inset Formula
\begin{equation}
\xihat\hat{p}=\left[\begin{array}{cc}
\skew{\omega} & v\\
0 & 0
\end{array}\right]\left[\begin{array}{c}
p\\
1
\end{array}\right]=\left[\begin{array}{c}
\skew{\omega}p+v\\
0
\end{array}\right]=\left[\begin{array}{cc}
I_{2} & R_{\pi/2}p\\
0 & 0
\end{array}\right]\left[\begin{array}{c}
v\\
\omega
\end{array}\right]=H(p)\xi\label{eq:HpSE2}
\end{equation}
\end_inset
Hence, by Theorem
\begin_inset CommandInset ref
LatexCommand ref
reference "th:Action"
\end_inset
we have
\begin_inset Formula
\begin{equation}
\deriv{\left(T\hat{p}\right)}{\xi}=TH(p)=\left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]\left[\begin{array}{cc}
I_{2} & R_{\pi/2}p\\
0 & 0
\end{array}\right]=\left[\begin{array}{cc}
R & RR_{\pi/2}p\\
0 & 0
\end{array}\right]=\left[\begin{array}{cc}
R & R_{\pi/2}q\\
0 & 0
\end{array}\right]\label{eq:SE2Action}
\end{equation}
\end_inset
Note that, looking only at the top rows of
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:HpSE2"
\end_inset
and
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:SE2Action"
\end_inset
, we can recognize the quantity
\begin_inset Formula $\skew{\omega}p+v=v+\omega\left(R_{\pi/2}p\right)$
\end_inset
as the velocity of
\begin_inset Formula $p$
\end_inset
in
\begin_inset Formula $\Rtwo$
\end_inset
, and
\begin_inset Formula $\left[\begin{array}{cc}
R & R_{\pi/2}q\end{array}\right]$
\end_inset
is the derivative of the action on
\begin_inset Formula $\Rtwo$
\end_inset
.
\end_layout
\begin_layout Standard
The derivative of the inverse action
\begin_inset Formula $g(T,p)=T^{-1}\hat{p}$
\end_inset
is given by Theorem
\begin_inset CommandInset ref
LatexCommand ref
reference "Th:InverseAction"
\end_inset
specialized to
\begin_inset Formula $\SEtwo$
\end_inset
:
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
\deriv{\left(T^{-1}\hat{p}\right)}{\xi}=-H(T^{-1}p)=\left[\begin{array}{cc}
-I_{2} & -R_{\pi/2}\left(T^{-1}p\right)\\
0 & 0
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Subsection
Pushforwards of Mappings
\end_layout
\begin_layout Standard
We can just define all derivatives in terms of the adjoint map, which in
the case of
\begin_inset Formula $\SEtwo$
\end_inset
, in twist coordinates, is the linear mapping
\begin_inset Formula
\[
\Ad T\xi=\left[\begin{array}{cc}
R & -R_{\pi/2}t\\
0 & 1
\end{array}\right]\left[\begin{array}{c}
v\\
\omega
\end{array}\right]
\]
\end_inset
and we have
\begin_inset Formula
\begin{eqnarray*}
\frac{\partial T^{^{-1}}}{\partial\xi} & = & -\Ad T
\end{eqnarray*}
\end_inset
\begin_inset Formula
\begin{eqnarray*}
\frac{\partial\left(T_{1}T_{2}\right)}{\partial\xi_{1}} & = & \Ad{T_{2}^{^{-1}}}\mbox{ and }\frac{\partial\left(T_{1}T_{2}\right)}{\partial\xi_{2}}=I_{3}
\end{eqnarray*}
\end_inset
\begin_inset Formula
\begin{eqnarray*}
\frac{\partial\left(T_{1}^{-1}T_{2}\right)}{\partial\xi_{1}} & = & -\Ad{T_{2}^{^{-1}}T_{1}}=-\Ad{between(T_{2},T_{1})}\mbox{ and }\frac{\partial\left(T_{1}^{-1}T_{2}\right)}{\partial\xi_{2}}=I_{3}
\end{eqnarray*}
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset
\end_layout
\begin_layout Section
3D Rotations
\end_layout
\begin_layout Subsection
Derivatives of Actions
\end_layout
\begin_layout Standard
In the case of
\begin_inset Formula $\SOthree$
\end_inset
the vector space is
\begin_inset Formula $\Rthree$
\end_inset
, and the group action
\begin_inset Formula $f(R,p)$
\end_inset
corresponds to rotating a point
\begin_inset Formula
\[
q=f(R,p)=Rp
\]
\end_inset
To calculate
\begin_inset Formula $H(p)$
\end_inset
for use in Theorem
\begin_inset CommandInset ref
LatexCommand eqref
reference "th:Action"
\end_inset
we make use of
\begin_inset Formula
\[
\Skew{\omega}p=\omega\times p=-p\times\omega=\Skew{-p}\omega
\]
\end_inset
so
\begin_inset Formula $H(p)\define\Skew{-p}$
\end_inset
.
Hence, the final derivative of an action in its first argument is
\begin_inset Formula
\[
\deriv{\left(Rp\right)}{\omega}=RH(p)=-R\Skew p
\]
\end_inset
Likewise, according to Theorem
\begin_inset CommandInset ref
LatexCommand ref
reference "Th:InverseAction"
\end_inset
, the derivative of the inverse action is given by
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
\deriv{\left(R^{T}p\right)}{\omega}=-H(R^{T}p)=\Skew{R^{T}p}
\]
\end_inset
\end_layout
\begin_layout Subsection
\begin_inset CommandInset label
LatexCommand label
name "sub:3DAngularVelocities"
\end_inset
Instantaneous Velocity
\end_layout
\begin_layout Standard
For 3D rotations
\begin_inset Formula $R_{b}^{n}$
\end_inset
from a body frame
\begin_inset Formula $b$
\end_inset
to a navigation frame
\begin_inset Formula $n$
\end_inset
we have the spatial angular velocity
\begin_inset Formula $\omega_{nb}^{n}$
\end_inset
measured in the navigation frame,
\begin_inset Formula
\[
\Skew{\omega_{nb}^{n}}\define\dot{R}_{b}^{n}\left(R_{b}^{n}\right)^{T}=\dot{R}_{b}^{n}R_{n}^{b}
\]
\end_inset
and the body angular velocity
\begin_inset Formula $\omega_{nb}^{b}$
\end_inset
measured in the body frame:
\begin_inset Formula
\[
\Skew{\omega_{nb}^{b}}\define\left(R_{b}^{n}\right)^{T}\dot{R}_{b}^{n}=R_{n}^{b}\dot{R}_{b}^{n}
\]
\end_inset
These quantities can be used to derive the velocity of a point
\begin_inset Formula $p$
\end_inset
, and we choose between spatial or body angular velocity depending on the
frame in which we choose to represent
\begin_inset Formula $p$
\end_inset
:
\begin_inset Formula
\[
v^{n}=\Skew{\omega_{nb}^{n}}p^{n}=\omega_{nb}^{n}\times p^{n}
\]
\end_inset
\begin_inset Formula
\[
v^{b}=\Skew{\omega_{nb}^{b}}p^{b}=\omega_{nb}^{b}\times p^{b}
\]
\end_inset
We can transform these skew-symmetric matrices from navigation to body frame
by conjugating,
\begin_inset Formula
\[
\Skew{\omega_{nb}^{b}}=R_{n}^{b}\Skew{\omega_{nb}^{n}}R_{b}^{n}
\]
\end_inset
but because the adjoint representation satisfies
\begin_inset Formula
\[
Ad_{R}\Skew{\omega}\define R\Skew{\omega}R^{T}=\Skew{R\omega}
\]
\end_inset
we can even more easily transform between spatial and body angular velocities
as 3-vectors:
\begin_inset Formula
\[
\omega_{nb}^{b}=R_{n}^{b}\omega_{nb}^{n}
\]
\end_inset
\end_layout
\begin_layout Subsection
Pushforwards of Mappings
\end_layout
\begin_layout Standard
For
\begin_inset Formula $\SOthree$
\end_inset
we have
\begin_inset Formula $\Ad R\Skew{\omega}=\Skew{R\omega}$
\end_inset
and, in terms of angular velocities:
\begin_inset Formula $\Ad R\omega=R\omega$
\end_inset
.
Hence, the Jacobian matrix of the
\series bold
inverse
\series default
mapping is (see Equation
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:Dinverse"
\end_inset
)
\begin_inset Formula
\[
\frac{\partial R^{T}}{\partial\omega}=-\Ad R=-R
\]
\end_inset
for
\series bold
compose
\series default
we have (Equations
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:Dcompose1"
\end_inset
and
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:Dcompose2"
\end_inset
):
\begin_inset Formula
\[
\frac{\partial\left(R_{1}R_{2}\right)}{\partial\omega_{1}}=R_{2}^{T}\mbox{ and }\frac{\partial\left(R_{1}R_{2}\right)}{\partial\omega_{2}}=I_{3}
\]
\end_inset
and
\series bold
between
\series default
(Equation
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:Dbetween1"
\end_inset
):
\begin_inset Formula
\[
\frac{\partial\left(R_{1}^{T}R_{2}\right)}{\partial\omega_{1}}=-R_{2}^{T}R_{1}=-between(R_{2},R_{1})\mbox{ and }\frac{\partial\left(R_{1}R_{2}\right)}{\partial\omega_{2}}=I_{3}
\]
\end_inset
\end_layout
\begin_layout Subsection
Retractions
\end_layout
\begin_layout Standard
Absil
\begin_inset CommandInset citation
LatexCommand cite
after "page 58"
key "Absil07book"
\end_inset
discusses two possible retractions for
\begin_inset Formula $\SOthree$
\end_inset
based on the QR decomposition or the polar decomposition of the matrix
\begin_inset Formula $R\Skew{\omega}$
\end_inset
, but they are expensive.
Another retraction is based on the Cayley transform
\begin_inset Formula $\mathcal{C}:\sothree\rightarrow\SOthree$
\end_inset
, a mapping from the skew-symmetric matrices to rotation matrices:
\begin_inset Formula
\[
Q=\mathcal{C}(\Omega)=(I-\Omega)(I+\Omega)^{-1}
\]
\end_inset
Interestingly, the inverse Cayley transform
\begin_inset Formula $\mathcal{C}^{-1}:\SOthree\rightarrow\sothree$
\end_inset
has the same form:
\begin_inset Formula
\[
\Omega=\mathcal{C}^{-1}(Q)=(I-Q)(I+Q)^{-1}
\]
\end_inset
The retraction needs a factor
\begin_inset Formula $-\frac{1}{2}$
\end_inset
however, to make it locally align with a geodesic:
\begin_inset Formula
\[
R'=\retract_{R}(\omega)=R\mathcal{C}(-\frac{1}{2}\Skew{\omega})
\]
\end_inset
Note that given
\begin_inset Formula $\omega=(x,y,z)$
\end_inset
this has the closed-form expression below
\begin_inset Formula
\[
\frac{1}{4+x^{2}+y^{2}+z^{2}}\left[\begin{array}{ccc}
4+x^{2}-y^{2}-z^{2} & 2xy-4z & 2xz+4y\\
2xy+4z & 4-x^{2}+y^{2}-z^{2} & 2yz-4x\\
2xz-4y & 2yz+4x & 4-x^{2}-y^{2}+z^{2}
\end{array}\right]
\]
\end_inset
\begin_inset Formula
\[
=\frac{1}{4+x^{2}+y^{2}+z^{2}}\left\{ 4(I+\Skew{\omega})+\left[\begin{array}{ccc}
x^{2}-y^{2}-z^{2} & 2xy & 2xz\\
2xy & -x^{2}+y^{2}-z^{2} & 2yz\\
2xz & 2yz & -x^{2}-y^{2}+z^{2}
\end{array}\right]\right\}
\]
\end_inset
so it can be seen to be a second-order correction on
\begin_inset Formula $(I+\Skew{\omega})$
\end_inset
.
The corresponding approximation to the logarithmic map is:
\begin_inset Formula
\[
\Skew{\omega}=\retract_{R}^{-1}(R')=-2\mathcal{C}^{-1}\left(R^{T}R'\right)
\]
\end_inset
\end_layout
\begin_layout Section
3D Rigid Transformations
\end_layout
\begin_layout Subsection
The derivatives of Actions
\end_layout
\begin_layout Standard
The action of
\begin_inset Formula $\SEthree$
\end_inset
on 3D points is done by embedding the points in
\begin_inset Formula $\mathbb{R}^{4}$
\end_inset
by using homogeneous coordinates
\begin_inset Formula
\[
\hat{q}=\left[\begin{array}{c}
q\\
1
\end{array}\right]=f(T,p)=\left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]\left[\begin{array}{c}
p\\
1
\end{array}\right]=T\hat{p}
\]
\end_inset
The quantity
\begin_inset Formula $\xihat\hat{p}$
\end_inset
corresponds to a velocity in
\begin_inset Formula $\mathbb{R}^{4}$
\end_inset
(in the local
\begin_inset Formula $T$
\end_inset
frame), and equating it to
\begin_inset Formula $H(p)\xi$
\end_inset
as in Equation
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:Hp"
\end_inset
yields the
\begin_inset Formula $4\times6$
\end_inset
matrix
\begin_inset Formula $H(p)$
\end_inset
\begin_inset Foot
status collapsed
\begin_layout Plain Layout
\begin_inset Formula $H(p)$
\end_inset
can also be obtained by taking the
\begin_inset Formula $j^{th}$
\end_inset
column of each of the 6 generators to multiply with components of
\begin_inset Formula $\hat{p}$
\end_inset
\end_layout
\end_inset
:
\begin_inset Formula
\[
\xihat\hat{p}=\left[\begin{array}{cc}
\Skew{\omega} & v\\
0 & 0
\end{array}\right]\left[\begin{array}{c}
p\\
1
\end{array}\right]=\left[\begin{array}{c}
\omega\times p+v\\
0
\end{array}\right]=\left[\begin{array}{cc}
\Skew{-p} & I_{3}\\
0 & 0
\end{array}\right]\left[\begin{array}{c}
\omega\\
v
\end{array}\right]=H(p)\xi
\]
\end_inset
Note how velocities are analogous to points at infinity in projective geometry:
they correspond to free vectors indicating a direction and magnitude of
change.
According to Theorem
\begin_inset CommandInset ref
LatexCommand ref
reference "th:Action"
\end_inset
, the derivative of the group action is then
\begin_inset Formula
\[
\deriv{\left(T\hat{p}\right)}{\xi}=TH(p)=\left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]\left[\begin{array}{cc}
\Skew{-p} & I_{3}\\
0 & 0
\end{array}\right]=\left[\begin{array}{cc}
R\Skew{-p} & R\\
0 & 0
\end{array}\right]
\]
\end_inset
\begin_inset Formula
\[
\deriv{\left(T\hat{p}\right)}{\hat{p}}=\left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]
\]
\end_inset
in homogenous coordinates.
In
\begin_inset Formula $\Rthree$
\end_inset
this becomes
\begin_inset Formula $R\left[\begin{array}{cc}
-\Skew p & I_{3}\end{array}\right]$
\end_inset
.
\end_layout
\begin_layout Standard
The derivative of the inverse action
\begin_inset Formula $T^{-1}p$
\end_inset
is given by Theorem
\begin_inset CommandInset ref
LatexCommand ref
reference "Th:InverseAction"
\end_inset
:
\end_layout
\begin_layout Standard
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\noun off
\color none
\begin_inset Formula
\[
\deriv{\left(T^{-1}\hat{p}\right)}{\xi}=-H\left(T^{-1}\hat{p}\right)=\left[\begin{array}{cc}
\Skew{T^{-1}\hat{p}} & -I_{3}\end{array}\right]
\]
\end_inset
\begin_inset Formula
\[
\deriv{\left(T^{-1}\hat{p}\right)}{\hat{p}}=\left[\begin{array}{cc}
R^{T} & -R^{T}t\\
0 & 1
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Example
Let us examine a visual SLAM example.
We have 2D measurements
\begin_inset Formula $z_{ij}$
\end_inset
, where each measurement is predicted by
\begin_inset Formula
\[
z_{ij}=h(T_{i},p_{j})=\pi(T_{i}^{-1}p_{j})=\pi(q)
\]
\end_inset
where
\begin_inset Formula $T_{i}$
\end_inset
is the 3D pose of the
\begin_inset Formula $i^{th}$
\end_inset
camera,
\begin_inset Formula $p_{j}$
\end_inset
is the location of the
\begin_inset Formula $j^{th}$
\end_inset
point,
\begin_inset Formula $q=(x',y',z')=T^{-1}p$
\end_inset
is the point in camera coordinates, and
\begin_inset Formula $\pi:(x,y,z)\mapsto(x/z,y/z)$
\end_inset
is the camera projection function from Example
\begin_inset CommandInset ref
LatexCommand ref
reference "ex:projection"
\end_inset
.
By the chain rule, we then have
\begin_inset Formula
\[
\deriv{h(T,p)}{\xi}=\deriv{\pi(q)}q\deriv{(T^{-1}p)}{\xi}=\frac{1}{z'}\left[\begin{array}{ccc}
1 & 0 & -x'/z'\\
0 & 1 & -y'/z'
\end{array}\right]\left[\begin{array}{cc}
\Skew q & -I_{3}\end{array}\right]=\left[\begin{array}{cc}
\pi'(q)\Skew q & -\pi'(q)\end{array}\right]
\]
\end_inset
\begin_inset Formula
\[
\deriv{h(T,p)}p=\pi'(q)R^{T}
\]
\end_inset
\end_layout
\begin_layout Subsection
Instantaneous Velocity
\end_layout
\begin_layout Standard
For rigid 3D transformations
\begin_inset Formula $T_{b}^{n}$
\end_inset
from a body frame
\begin_inset Formula $b$
\end_inset
to a navigation frame
\begin_inset Formula $n$
\end_inset
we have the instantaneous spatial twist
\begin_inset Formula $\xi_{nb}^{n}$
\end_inset
measured in the navigation frame,
\begin_inset Formula
\[
\hat{\xi}_{nb}^{n}\define\dot{T}_{b}^{n}\left(T_{b}^{n}\right)^{-1}
\]
\end_inset
and the instantaneous body twist
\begin_inset Formula $\xi_{nb}^{b}$
\end_inset
measured in the body frame:
\begin_inset Formula
\[
\hat{\xi}_{nb}^{b}\define\left(T_{b}^{n}\right)^{T}\dot{T}_{b}^{n}
\]
\end_inset
\end_layout
\begin_layout Subsection
Pushforwards of Mappings
\end_layout
\begin_layout Standard
As we can express the Adjoint representation in terms of twist coordinates,
we have
\begin_inset Formula
\[
\left[\begin{array}{c}
\omega'\\
v'
\end{array}\right]=\left[\begin{array}{cc}
R & 0\\
\Skew tR & R
\end{array}\right]\left[\begin{array}{c}
\omega\\
v
\end{array}\right]
\]
\end_inset
Hence, as with
\begin_inset Formula $\SOthree$
\end_inset
, we are now in a position to simply posit the derivative of
\series bold
inverse
\series default
,
\begin_inset Formula
\[
\frac{\partial T^{-1}}{\partial\xi}=\Ad T=-\left[\begin{array}{cc}
R & 0\\
\Skew tR & R
\end{array}\right]
\]
\end_inset
\series bold
compose
\series default
in its first argument,
\begin_inset Formula
\[
\frac{\partial\left(T_{1}T_{2}\right)}{\partial\xi_{1}}=\Ad{T_{2}^{-1}}
\]
\end_inset
in its second argument,
\begin_inset Formula
\[
\frac{\partial\left(T_{1}T_{2}\right)}{\partial\xi_{2}}=I_{6}
\]
\end_inset
\series bold
between
\series default
in its first argument,
\begin_inset Formula
\[
\frac{\partial\left(T_{1}^{^{-1}}T_{2}\right)}{\partial\xi_{1}}=\Ad{T_{2}^{^{-1}}T_{1}}
\]
\end_inset
and in its second argument,
\begin_inset Formula
\begin{eqnarray*}
\frac{\partial\left(T_{1}^{^{-1}}T_{2}\right)}{\partial\xi_{1}} & = & I_{6}
\end{eqnarray*}
\end_inset
\end_layout
\begin_layout Subsection
Retractions
\end_layout
\begin_layout Standard
For
\begin_inset Formula $\SEthree$
\end_inset
, instead of using the true exponential map it is computationally more efficient
to design other retractions.
A first-order approximation to the exponential map does not quite cut it,
as it yields a
\begin_inset Formula $4\times4$
\end_inset
matrix which is not in
\begin_inset Formula $\SEthree$
\end_inset
:
\begin_inset Formula
\begin{eqnarray*}
T\exp\xihat & \approx & T(I+\xihat)\\
& = & T\left(I_{4}+\left[\begin{array}{cc}
\Skew{\omega} & v\\
0 & 0
\end{array}\right]\right)\\
& = & \left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]\left[\begin{array}{cc}
I_{3}+\Skew{\omega} & v\\
0 & 1
\end{array}\right]\\
& = & \left[\begin{array}{cc}
R\left(I_{3}+\Skew{\omega}\right) & t+Rv\\
0 & 1
\end{array}\right]
\end{eqnarray*}
\end_inset
However, we can make it into a retraction by using any retraction defined
for
\begin_inset Formula $\SOthree$
\end_inset
, including, as below, using the exponential map
\begin_inset Formula $Re^{\Skew{\omega}}$
\end_inset
:
\begin_inset Formula
\[
\retract_{T}\left(\left[\begin{array}{c}
\omega\\
v
\end{array}\right]\right)=\left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]\left[\begin{array}{cc}
e^{\Skew{\omega}} & v\\
0 & 1
\end{array}\right]=\left[\begin{array}{cc}
Re^{\Skew{\omega}} & t+Rv\\
0 & 1
\end{array}\right]
\]
\end_inset
Similarly, for a second order approximation we have
\begin_inset Formula
\begin{eqnarray*}
T\exp\xihat & \approx & T(I+\xihat+\frac{\xihat^{2}}{2})\\
& = & T\left(I_{4}+\left[\begin{array}{cc}
\Skew{\omega} & v\\
0 & 0
\end{array}\right]+\frac{1}{2}\left[\begin{array}{cc}
\Skew{\omega} & v\\
0 & 0
\end{array}\right]\left[\begin{array}{cc}
\Skew{\omega} & v\\
0 & 0
\end{array}\right]\right)\\
& = & \left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]\left(\left[\begin{array}{cc}
I_{3}+\Skew{\omega}+\frac{1}{2}\Skew{\omega}^{2} & v+\frac{1}{2}\Skew{\omega}v\\
0 & 1
\end{array}\right]\right)\\
& = & \left[\begin{array}{cc}
R\left(I_{3}+\Skew{\omega}+\frac{1}{2}\Skew{\omega}^{2}\right) & t+R\left[v+\left(\omega\times v\right)/2\right]\\
0 & 1
\end{array}\right]
\end{eqnarray*}
\end_inset
inspiring the retraction
\begin_inset Formula
\[
\retract_{T}\left(\left[\begin{array}{c}
\omega\\
v
\end{array}\right]\right)=\left[\begin{array}{cc}
R & t\\
0 & 1
\end{array}\right]\left[\begin{array}{cc}
e^{\Skew{\omega}} & v+\left(\omega\times v\right)/2\\
0 & 1
\end{array}\right]=\left[\begin{array}{cc}
Re^{\Skew{\omega}} & t+R\left[v+\left(\omega\times v\right)/2\right]\\
0 & 1
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset
\end_layout
\begin_layout Section
2D Line Segments (Ocaml)
\end_layout
\begin_layout Standard
The error between an infinite line
\begin_inset Formula $(a,b,c)$
\end_inset
and a 2D line segment
\begin_inset Formula $((x1,y1),(x2,y2))$
\end_inset
is defined in Line3.ml.
\end_layout
\begin_layout Section
Line3vd (Ocaml)
\end_layout
\begin_layout Standard
One representation of a line is through 2 vectors
\begin_inset Formula $(v,d)$
\end_inset
, where
\begin_inset Formula $v$
\end_inset
is the direction and the vector
\begin_inset Formula $d$
\end_inset
points from the orgin to the closest point on the line.
\end_layout
\begin_layout Standard
In this representation, transforming a 3D line from a world coordinate frame
to a camera at
\begin_inset Formula $(R_{w}^{c},t^{w})$
\end_inset
is done by
\begin_inset Formula
\[
v^{c}=R_{w}^{c}v^{w}
\]
\end_inset
\begin_inset Formula
\[
d^{c}=R_{w}^{c}\left(d^{w}+(t^{w}v^{w})v^{w}-t^{w}\right)
\]
\end_inset
\end_layout
\begin_layout Section
Line3 (Ocaml)
\end_layout
\begin_layout Standard
For 3D lines, we use a parameterization due to C.J.
Taylor, using a rotation matrix
\begin_inset Formula $R$
\end_inset
and 2 scalars
\begin_inset Formula $a$
\end_inset
and
\begin_inset Formula $b$
\end_inset
.
The line direction
\begin_inset Formula $v$
\end_inset
is simply the Z-axis of the rotated frame, i.e.,
\begin_inset Formula $v=R_{3}$
\end_inset
, while the vector
\begin_inset Formula $d$
\end_inset
is given by
\begin_inset Formula $d=aR_{1}+bR_{2}$
\end_inset
.
\end_layout
\begin_layout Standard
Now, we will
\emph on
not
\emph default
use the incremental rotation scheme we used for rotations: because the
matrix R translates from the line coordinate frame to the world frame,
we need to apply the incremental rotation on the right-side:
\begin_inset Formula
\[
R'=R(I+\Omega)
\]
\end_inset
Projecting a line to 2D can be done easily, as both
\begin_inset Formula $v$
\end_inset
and
\begin_inset Formula $d$
\end_inset
are also the 2D homogenous coordinates of two points on the projected line,
and hence we have
\begin_inset Formula
\begin{eqnarray*}
l & = & v\times d\\
& = & R_{3}\times\left(aR_{1}+bR_{2}\right)\\
& = & a\left(R_{3}\times R_{1}\right)+b\left(R_{3}\times R_{2}\right)\\
& = & aR_{2}-bR_{1}
\end{eqnarray*}
\end_inset
This can be written as a rotation of a point,
\begin_inset Formula
\[
l=R\left(\begin{array}{c}
-b\\
a\\
0
\end{array}\right)
\]
\end_inset
but because the incremental rotation is now done on the right, we need to
figure out the derivatives again:
\begin_inset Formula
\begin{equation}
\frac{\partial(R(I+\Omega)x)}{\partial\omega}=\frac{\partial(R\Omega x)}{\partial\omega}=R\frac{\partial(\Omega x)}{\partial\omega}=R\Skew{-x}\label{eq:rotateRight}
\end{equation}
\end_inset
and hence the derivative of the projection
\begin_inset Formula $l$
\end_inset
with respect to the rotation matrix
\begin_inset Formula $R$
\end_inset
of the 3D line is
\begin_inset Formula
\begin{equation}
\frac{\partial(l)}{\partial\omega}=R\Skew{\left(\begin{array}{c}
b\\
-a\\
0
\end{array}\right)}=\left[\begin{array}{ccc}
aR_{3} & bR_{3} & -(aR_{1}+bR_{2})\end{array}\right]
\end{equation}
\end_inset
or the
\begin_inset Formula $a,b$
\end_inset
scalars:
\begin_inset Formula
\[
\frac{\partial(l)}{\partial a}=R_{2}
\]
\end_inset
\begin_inset Formula
\[
\frac{\partial(l)}{\partial b}=-R_{1}
\]
\end_inset
\end_layout
\begin_layout Standard
Transforming a 3D line
\begin_inset Formula $(R,(a,b))$
\end_inset
from a world coordinate frame to a camera frame
\begin_inset Formula $(R_{w}^{c},t^{w})$
\end_inset
is done by
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
R'=R_{w}^{c}R
\]
\end_inset
\begin_inset Formula
\[
a'=a-R_{1}^{T}t^{w}
\]
\end_inset
\begin_inset Formula
\[
b'=b-R_{2}^{T}t^{w}
\]
\end_inset
Again, we need to redo the derivatives, as R is incremented from the right.
The first argument is incremented from the left, but the result is incremented
on the right:
\begin_inset Formula
\begin{eqnarray*}
R'(I+\Omega')=(AB)(I+\Omega') & = & (I+\Skew{S\omega})AB\\
I+\Omega' & = & (AB)^{T}(I+\Skew{S\omega})(AB)\\
\Omega' & = & R'^{T}\Skew{S\omega}R'\\
\Omega' & = & \Skew{R'^{T}S\omega}\\
\omega' & = & R'^{T}S\omega
\end{eqnarray*}
\end_inset
For the second argument
\begin_inset Formula $R$
\end_inset
we now simply have:
\begin_inset Formula
\begin{eqnarray*}
AB(I+\Omega') & = & AB(I+\Omega)\\
\Omega' & = & \Omega\\
\omega' & = & \omega
\end{eqnarray*}
\end_inset
The scalar derivatives can be found by realizing that
\begin_inset Formula
\[
\left(\begin{array}{c}
a'\\
b'\\
...
\end{array}\right)=\left(\begin{array}{c}
a\\
b\\
0
\end{array}\right)-R^{T}t^{w}
\]
\end_inset
where we don't care about the third row.
Hence
\begin_inset Formula
\[
\frac{\partial(\left(R(I+\Omega_{2})\right)^{T}t^{w})}{\partial\omega}=-\frac{\partial(\Omega_{2}R^{T}t^{w})}{\partial\omega}=-\Skew{R^{T}t^{w}}=\left[\begin{array}{ccc}
0 & R_{3}^{T}t^{w} & -R_{2}^{T}t^{w}\\
-R_{3}^{T}t^{w} & 0 & R_{1}^{T}t^{w}\\
... & ... & 0
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Section
\series bold
Aligning 3D Scans
\end_layout
\begin_layout Standard
Below is the explanaition underlying Pose3.align, i.e.
aligning two point clouds using SVD.
Inspired but modified from CVOnline...
\end_layout
\begin_layout Standard
\emph on
Our
\emph default
model is
\begin_inset Formula
\[
p^{c}=R\left(p^{w}-t\right)
\]
\end_inset
i.e.,
\begin_inset Formula $R$
\end_inset
is from camera to world, and
\begin_inset Formula $t$
\end_inset
is the camera location in world coordinates.
The objective function is
\begin_inset Formula
\begin{equation}
\frac{1}{2}\sum\left(p^{c}-R(p^{w}-t)\right)^{2}=\frac{1}{2}\sum\left(p^{c}-Rp^{w}+Rt\right)^{2}=\frac{1}{2}\sum\left(p^{c}-Rp^{w}-t'\right)^{2}\label{eq:J}
\end{equation}
\end_inset
where
\begin_inset Formula $t'=-Rt$
\end_inset
is the location of the origin in the camera frame.
Taking the derivative with respect to
\begin_inset Formula $t'$
\end_inset
and setting to zero we have
\begin_inset Formula
\[
\sum\left(p^{c}-Rp^{w}-t'\right)=0
\]
\end_inset
or
\begin_inset Formula
\begin{equation}
t'=\frac{1}{n}\sum\left(p^{c}-Rp^{w}\right)=\bar{p}^{c}-R\bar{p}^{w}\label{eq:t}
\end{equation}
\end_inset
here
\begin_inset Formula $\bar{p}^{c}$
\end_inset
and
\begin_inset Formula $\bar{p}^{w}$
\end_inset
are the point cloud centroids.
Substituting back into
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:J"
\end_inset
, we get
\begin_inset Formula
\[
\frac{1}{2}\sum\left(p^{c}-R(p^{w}-t)\right)^{2}=\frac{1}{2}\sum\left(\left(p^{c}-\bar{p}^{c}\right)-R\left(p^{w}-\bar{p}^{w}\right)\right)^{2}=\frac{1}{2}\sum\left(\hat{p}^{c}-R\hat{p}^{w}\right)^{2}
\]
\end_inset
Now, to minimize the above it suffices to maximize (see CVOnline)
\begin_inset Formula
\[
\mathop{trace}\left(R^{T}C\right)
\]
\end_inset
where
\begin_inset Formula $C=\sum\hat{p}^{c}\left(\hat{p}^{w}\right)^{T}$
\end_inset
is the correlation matrix.
Intuitively, the cloud of points is rotated to align with the principal
axes.
This can be achieved by SVD decomposition on
\begin_inset Formula $C$
\end_inset
\begin_inset Formula
\[
C=USV^{T}
\]
\end_inset
and setting
\begin_inset Formula
\[
R=UV^{T}
\]
\end_inset
Clearly, from
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:t"
\end_inset
we then also recover the optimal
\begin_inset Formula $t$
\end_inset
as
\begin_inset Formula
\[
t=\bar{p}^{w}-R^{T}\bar{p}^{c}
\]
\end_inset
\end_layout
\begin_layout Section*
Appendix
\end_layout
\begin_layout Subsection*
Differentiation Rules
\end_layout
\begin_layout Standard
Spivak
\begin_inset CommandInset citation
LatexCommand cite
key "Spivak65book"
\end_inset
also notes some multivariate derivative rules defined component-wise, but
they are not that useful in practice:
\end_layout
\begin_layout Itemize
Since
\begin_inset Formula $f:\Multi nm$
\end_inset
is defined in terms of
\begin_inset Formula $m$
\end_inset
component functions
\begin_inset Formula $f^{i}$
\end_inset
, then
\begin_inset Formula $f$
\end_inset
is differentiable at
\begin_inset Formula $a$
\end_inset
iff each
\begin_inset Formula $f^{i}$
\end_inset
is, and the Jacobian matrix
\begin_inset Formula $F_{a}$
\end_inset
is the
\begin_inset Formula $m\times n$
\end_inset
matrix whose
\begin_inset Formula $i^{th}$
\end_inset
row is
\begin_inset Formula $\left(f^{i}\right)'(a)$
\end_inset
:
\begin_inset Formula
\[
F_{a}\define f'(a)=\left[\begin{array}{c}
\left(f^{1}\right)'(a)\\
\vdots\\
\left(f^{m}\right)'(a)
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Itemize
Scalar differentiation rules: if
\begin_inset Formula $f,g:\OneD n$
\end_inset
are differentiable at
\begin_inset Formula $a$
\end_inset
, then
\begin_inset Formula
\[
(f+g)'(a)=F_{a}+G_{a}
\]
\end_inset
\begin_inset Formula
\[
(f\cdot g)'(a)=g(a)F_{a}+f(a)G_{a}
\]
\end_inset
\begin_inset Formula
\[
(f/g)'(a)=\frac{1}{g(a)^{2}}\left[g(a)F_{a}-f(a)G_{a}\right]
\]
\end_inset
\end_layout
\begin_layout Subsection*
Tangent Spaces and the Tangent Bundle
\end_layout
\begin_layout Standard
The following is adapted from Appendix A in
\begin_inset CommandInset citation
LatexCommand cite
key "Murray94book"
\end_inset
.
\end_layout
\begin_layout Standard
The
\series bold
tangent space
\series default
\begin_inset Formula $T_{p}M$
\end_inset
of a manifold
\begin_inset Formula $M$
\end_inset
at a point
\begin_inset Formula $p\in M$
\end_inset
is the vector space of
\series bold
tangent vectors
\series default
at
\begin_inset Formula $p$
\end_inset
.
The
\series bold
tangent bundle
\series default
\begin_inset Formula $TM$
\end_inset
is the set of all tangent vectors
\begin_inset Formula
\[
TM\define\bigcup_{p\in M}T_{p}M
\]
\end_inset
A
\series bold
vector field
\series default
\begin_inset Formula $X:M\rightarrow TM$
\end_inset
assigns a single tangent vector
\begin_inset Formula $x\in T_{p}M$
\end_inset
to each point
\begin_inset Formula $p$
\end_inset
.
\end_layout
\begin_layout Standard
If
\begin_inset Formula $F:M\rightarrow N$
\end_inset
is a smooth map from a manifold
\begin_inset Formula $M$
\end_inset
to a manifold
\begin_inset Formula $N$
\end_inset
, then we can define the
\series bold
tangent map
\series default
of
\begin_inset Formula $F$
\end_inset
at
\begin_inset Formula $p$
\end_inset
as the linear map
\begin_inset Formula $F_{*p}:T_{p}M\rightarrow T_{F(p)}N$
\end_inset
that maps tangent vectors in
\begin_inset Formula $T_{p}M$
\end_inset
at
\begin_inset Formula $p$
\end_inset
to tangent vectors in
\begin_inset Formula $T_{F(p)}N$
\end_inset
at the image
\begin_inset Formula $F(p)$
\end_inset
.
\end_layout
\begin_layout Subsection*
Homomorphisms
\end_layout
\begin_layout Standard
The following
\emph on
might be
\emph default
relevant
\begin_inset CommandInset citation
LatexCommand cite
after "page 45"
key "Hall00book"
\end_inset
: suppose that
\begin_inset Formula $\Phi:G\rightarrow H$
\end_inset
is a mapping (Lie group homomorphism).
Then there exists a unique linear map
\begin_inset Formula $\phi:\gg\rightarrow\mathfrak{h}$
\end_inset
\begin_inset Formula
\[
\phi(\xhat)\define\lim_{t\rightarrow0}\frac{d}{dt}\Phi\left(e^{t\xhat}\right)
\]
\end_inset
such that
\end_layout
\begin_layout Enumerate
\begin_inset Formula $\Phi\left(e^{\xhat}\right)=e^{\phi\left(\xhat\right)}$
\end_inset
\end_layout
\begin_layout Enumerate
\begin_inset Formula $\phi\left(T\xhat T^{-1}\right)=\Phi(T)\phi(\xhat)\Phi(T^{-1})$
\end_inset
\end_layout
\begin_layout Enumerate
\begin_inset Formula $\phi\left([\xhat,\yhat]\right)=\left[\phi(\xhat),\phi(\yhat)\right]$
\end_inset
\end_layout
\begin_layout Standard
In other words, the map
\begin_inset Formula $\phi$
\end_inset
is the derivative of
\begin_inset Formula $\Phi$
\end_inset
at the identity.
As an example, suppose
\begin_inset Formula $\Phi(g)=g^{-1}$
\end_inset
, then the corresponding derivative
\emph on
at the identity
\emph default
is
\begin_inset Formula
\[
\phi(\xhat)\define\lim_{t\rightarrow0}\frac{d}{dt}\left(e^{t\xhat}\right)^{-1}=\lim_{t\rightarrow0}\frac{d}{dt}e^{-t\xhat}=-\xhat\lim_{t\rightarrow0}e^{-t\xhat}=-\xhat
\]
\end_inset
In general it suffices to compute
\begin_inset Formula $\phi$
\end_inset
for a basis of
\begin_inset Formula $\gg$
\end_inset
.
\end_layout
\begin_layout Standard
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Undercooked: What if we want the derivative of
\begin_inset Formula $\Phi$
\end_inset
at some other element
\begin_inset Formula $g$
\end_inset
? In other words, if we apply
\begin_inset Formula $\Phi$
\end_inset
at
\begin_inset Formula $g$
\end_inset
incremented by some Lie algebra element
\begin_inset Formula $e^{\xhat}$
\end_inset
, then we are looking for a
\begin_inset Formula $\yhat\in\gg$
\end_inset
will yield the same result:
\begin_inset Formula
\[
\Phi\left(g\right)\lim_{t\rightarrow0}\frac{d}{dt}e^{t\yhat}=\lim_{t\rightarrow0}\frac{d}{dt}\Phi\left(ge^{t\xhat}\right)
\]
\end_inset
\begin_inset Formula
\[
\lim_{t\rightarrow0}\frac{d}{dt}e^{t\yhat}=\Phi\left(g\right)^{-1}\lim_{t\rightarrow0}\frac{d}{dt}\Phi\left(ge^{t\xhat}\right)
\]
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Note Note
status collapsed
\begin_layout Plain Layout
Let us define two mappings
\begin_inset Formula
\[
\Phi_{1}(A)=AB\mbox{ and }\Phi_{2}(B)=AB
\]
\end_inset
Then
\begin_inset Formula
\[
\phi_{1}(\xhat)=\lim_{t\rightarrow0}\frac{d}{dt}\Phi_{1}\left(e^{t\xhat}B\right)=
\]
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset CommandInset bibtex
LatexCommand bibtex
bibfiles "/Users/dellaert/papers/refs"
options "plain"
\end_inset
\end_layout
\end_body
\end_document