gtsam/doc/ImuFactor.lyx

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\begin_body
\begin_layout Title
The new IMU Factor
\end_layout
\begin_layout Author
Frank Dellaert & Varun Agrawal
\end_layout
\begin_layout Standard
\begin_inset CommandInset include
LatexCommand include
filename "macros.lyx"
\end_inset
\end_layout
\begin_layout Standard
\begin_inset FormulaMacro
\renewcommand{\sothree}{\mathfrak{so(3)}}
{\mathfrak{so(3)}}
\end_inset
\end_layout
\begin_layout Subsubsection*
Navigation States
\end_layout
\begin_layout Standard
Let us assume a setup where frames with image and/or laser measurements
are processed at some fairly low rate, e.g., 10 Hz.
\end_layout
\begin_layout Standard
We define the state of the vehicle at those times as attitude, position,
and velocity.
These three quantities are jointly referred to as a NavState
\begin_inset Formula $X_{b}^{n}\define\left\{ R_{b}^{n},P_{b}^{n},V_{b}^{n}\right\} $
\end_inset
, where the superscript
\begin_inset Formula $n$
\end_inset
denotes the
\emph on
navigation frame
\emph default
, and
\begin_inset Formula $b$
\end_inset
the
\emph on
body frame
\emph default
.
For simplicity, we drop these indices below where clear from context.
\end_layout
\begin_layout Subsubsection*
Vector Fields and Differential Equations
\end_layout
\begin_layout Standard
We need a way to describe the evolution of a NavState over time.
The NavState lives in a 9-dimensional manifold
\begin_inset Formula $M$
\end_inset
, defined by the orthonormality constraints on
\begin_inset Formula $\Rone$
\end_inset
.
For a NavState
\begin_inset Formula $X$
\end_inset
evolving over time we can write down a differential equation
\begin_inset Formula
\begin{equation}
\dot{X}(t)=F(t,X)\label{eq:diffeqM}
\end{equation}
\end_inset
where
\begin_inset Formula $F$
\end_inset
is a time-varying
\series bold
vector field
\series default
on
\begin_inset Formula $M$
\end_inset
, defined as a mapping from
\begin_inset Formula $\Rone\times M$
\end_inset
to tangent vectors at
\begin_inset Formula $X$
\end_inset
.
A
\series bold
tangent vector
\series default
at
\begin_inset Formula $X$
\end_inset
is defined as the derivative of a trajectory at
\begin_inset Formula $X$
\end_inset
, and for the NavState manifold this will be a triplet
\begin_inset Formula
\[
\left[\dot{R}(t,X),\dot{P}(t,X),\dot{V}(t,X)\right]\in\sothree\times\Rthree\times\Rthree
\]
\end_inset
where we use square brackets to indicate a tangent vector.
The space of all tangent vectors at
\begin_inset Formula $X$
\end_inset
is denoted by
\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 $T_{X}M$
\end_inset
\family default
\series default
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit
, and hence
\begin_inset Formula $F(t,X)\in T_{X}M$
\end_inset
.
For example, if the state evolves along a constant velocity trajectory
\begin_inset Formula
\[
X(t)=\left\{ R_{0},P_{0}+V_{0}t,V_{0}\right\}
\]
\end_inset
then the differential equation describing the trajectory is
\begin_inset Formula
\[
\dot{X}(t)=\left[0_{3x3},V_{0},0_{3x1}\right],\,\,\,\,\,X(0)=\left\{ R_{0},P_{0},V_{0}\right\}
\]
\end_inset
\end_layout
\begin_layout Standard
Valid vector fields on a NavState manifold are special, in that the attitude
and velocity derivatives can be arbitrary functions of X and t, but the
derivative of position is constrained to be equal to the current velocity
\begin_inset Formula $V(t)$
\end_inset
:
\begin_inset Formula
\begin{equation}
\dot{X}(t)=\left[\dot{R}(X,t),V(t),\dot{V}(X,t)\right]\label{eq:validField}
\end{equation}
\end_inset
Suppose we are given the
\series bold
body angular velocity
\series default
\begin_inset Formula $\omega^{b}(t)$
\end_inset
and non-gravity
\series bold
acceleration
\series default
\begin_inset Formula $a^{b}(t)$
\end_inset
in the body frame.
We know (from Murray84book) that the derivative of
\begin_inset Formula $R$
\end_inset
can be written as
\begin_inset Formula
\[
\dot{R}(X,t)=R(t)\Skew{\omega^{b}(t)}
\]
\end_inset
where
\begin_inset Formula $\Skew{\theta}\in so(3)$
\end_inset
is the skew-symmetric matrix corresponding to
\begin_inset Formula $\theta$
\end_inset
, and hence the resulting exact vector field is
\begin_inset Formula
\begin{equation}
\dot{X}(t)=\left[\dot{R}(X,t),V(t),\dot{V}(X,t)\right]=\left[R(t)\Skew{\omega^{b}(t)},V(t),g+R(t)a^{b}(t)\right]\label{eq:bodyField}
\end{equation}
\end_inset
\end_layout
\begin_layout Subsubsection*
Local Coordinates
\end_layout
\begin_layout Standard
Optimization on manifolds relies crucially on the concept of
\series bold
local coordinates
\series default
.
For example, when optimizing over the rotations
\begin_inset Formula $\SOthree$
\end_inset
starting from an initial estimate
\begin_inset Formula $R_{0}$
\end_inset
, we define a local map
\begin_inset Formula $\Phi_{R_{0}}$
\end_inset
from
\begin_inset Formula $\theta\in\Rthree$
\end_inset
to a neighborhood of
\begin_inset Formula $\SOthree$
\end_inset
centered around
\begin_inset Formula $R_{0}$
\end_inset
,
\begin_inset Formula
\[
\Phi_{R_{0}}(\theta)=R_{0}\exp\left(\Skew{\theta}\right)
\]
\end_inset
where
\begin_inset Formula $\exp$
\end_inset
is the matrix exponential, given by
\begin_inset Formula
\begin{equation}
\exp\left(\Skew{\theta}\right)=\sum_{k=0}^{\infty}\frac{1}{k!}\Skew{\theta}^{k}\label{eq:expm}
\end{equation}
\end_inset
which for
\begin_inset Formula $\SOthree$
\end_inset
can be efficiently computed in closed form.
\end_layout
\begin_layout Standard
The local coordinates
\begin_inset Formula $\theta$
\end_inset
are isomorphic to tangent vectors at
\emph on
\begin_inset Formula $R_{0}$
\end_inset
\emph default
.
To see this, define
\begin_inset Formula $\theta=\omega t$
\end_inset
and note that
\begin_inset Formula
\[
\frac{d\Phi_{R_{0}}\left(\omega t\right)}{dt}\biggr\vert_{t=0}=\frac{dR_{0}\exp\left(\Skew{\omega t}\right)}{dt}\biggr\vert_{t=0}=R_{0}\Skew{\omega t}
\]
\end_inset
Hence, the 3-vector
\begin_inset Formula $\omega$
\end_inset
defines a direction of travel on the
\begin_inset Formula $\SOthree$
\end_inset
manifold, but does so in the local coordinate frame define by
\begin_inset Formula $R_{0}$
\end_inset
.
\end_layout
\begin_layout Standard
A similar story holds in
\begin_inset Formula $\SEthree$
\end_inset
: we define local coordinates
\begin_inset Formula $\xi=\left[\omega t,vt\right]\in\Rsix$
\end_inset
and a mapping
\begin_inset Formula
\[
\Phi_{T_{0}}(\xi)=T_{0}\exp\xihat
\]
\end_inset
where
\begin_inset Formula $\xihat\in\sethree$
\end_inset
is defined as
\begin_inset Formula
\[
\xihat=\left[\begin{array}{cc}
\Skew{\omega} & v\\
0 & 0
\end{array}\right]t
\]
\end_inset
and the 6-vectors
\begin_inset Formula $\xi$
\end_inset
are mapped to tangent vectors
\begin_inset Formula $T_{0}\xihat$
\end_inset
at
\begin_inset Formula $T_{0}$
\end_inset
.
\end_layout
\begin_layout Subsubsection*
Derivative of The Local Coordinate Mapping
\end_layout
\begin_layout Standard
For the local coordinate mapping
\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 $\Phi_{R_{0}}\left(\theta\right)$
\end_inset
\family default
\series default
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit
in
\begin_inset Formula $\SOthree$
\end_inset
we can define a
\begin_inset Formula $3\times3$
\end_inset
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none
Jacobian
\begin_inset Formula $H(\theta)$
\end_inset
that models the effect of an incremental change
\begin_inset Formula $\delta$
\end_inset
to the local coordinates:
\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}
\Phi_{R_{0}}\left(\theta+\delta\right)\approx\Phi_{R_{0}}\left(\theta\right)\,\exp\left(\Skew{H(\theta)\delta}\right)=\Phi_{\Phi_{R_{0}}\left(\theta\right)}\left(H(\theta)\delta\right)\label{eq:push_exp}
\end{equation}
\end_inset
This Jacobian depends only on
\begin_inset Formula $\theta$
\end_inset
and, for the case of
\begin_inset Formula $\SOthree$
\end_inset
, is given by a formula similar to the matrix exponential map,
\begin_inset Formula
\[
H(\theta)=\sum_{k=0}^{\infty}\frac{(-1)^{k}}{(k+1)!}\Skew{\theta}^{k}
\]
\end_inset
which can also be computed in closed form.
In particular,
\begin_inset Formula $H(0)=I_{3\times3}$
\end_inset
at the base
\begin_inset Formula $R_{0}$
\end_inset
.
\end_layout
\begin_layout Subsubsection*
Numerical Integration in Local Coordinates
\end_layout
\begin_layout Standard
Inspired by the paper
\begin_inset Quotes eld
\end_inset
Lie Group Methods
\begin_inset Quotes erd
\end_inset
by Iserles et al.
\begin_inset CommandInset citation
LatexCommand cite
key "Iserles00an"
literal "true"
\end_inset
, when we have a differential equation on
\begin_inset Formula $\SOthree$
\end_inset
,
\begin_inset Formula
\begin{equation}
\dot{R}(t)=F(R,t),\,\,\,\,R(0)=R_{0}\label{eq:diffSo3}
\end{equation}
\end_inset
we can transfer it to a differential equation in the 3-dimensional local
coordinate space.
To do so, we model the solution to
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:diffSo3"
\end_inset
as
\begin_inset Formula
\[
R(t)=\Phi_{R_{0}}(\theta(t))
\]
\end_inset
To find an expression for
\begin_inset Formula $\dot{\theta}(t)$
\end_inset
, create a trajectory
\begin_inset Formula $\gamma(\delta)$
\end_inset
that passes through
\begin_inset Formula $R(t)$
\end_inset
for
\begin_inset Formula $\delta=0$
\end_inset
, and moves
\begin_inset Formula $\theta(t)$
\end_inset
along the direction
\begin_inset Formula $\dot{\theta}(t)$
\end_inset
:
\begin_inset Formula
\[
\gamma(\delta)=R(t+\delta)=\Phi_{R_{0}}\left(\theta(t)+\dot{\theta}(t)\delta\right)\approx\Phi_{R(t)}\left(H(\theta)\dot{\theta}(t)\delta\right)
\]
\end_inset
Taking the derivative for
\begin_inset Formula $\delta=0$
\end_inset
we obtain
\begin_inset Formula
\[
\dot{R}(t)=\frac{d\gamma(\delta)}{d\delta}\biggr\vert_{\delta=0}=\frac{d\Phi_{R(t)}\left(H(\theta)\dot{\theta}(t)\delta\right)}{d\delta}\biggr\vert_{\delta=0}=R(t)\Skew{H(\theta)\dot{\theta}(t)}
\]
\end_inset
Comparing this to
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:diffSo3"
\end_inset
we obtain a differential equation for
\begin_inset Formula $\theta(t)$
\end_inset
:
\begin_inset Formula
\[
\dot{\theta}(t)=H(\theta)^{-1}\left\{ R(t)^{T}F(R,t)\right\} \check{},\,\,\,\,\theta(0)=0_{3\times1}
\]
\end_inset
In other words, the vector field
\begin_inset Formula $F(R,t)$
\end_inset
is rotated to the local frame, the inverse hat operator is applied to get
a 3-vector, which is then corrected by
\begin_inset Formula $H(\theta)^{-1}$
\end_inset
away from
\begin_inset Formula $\theta=0$
\end_inset
.
\end_layout
\begin_layout Subsubsection*
Retractions
\end_layout
\begin_layout Standard
\begin_inset FormulaMacro
\newcommand{\Rnine}{\mathfrak{\mathbb{R}^{9}}}
{\mathfrak{\mathbb{R}^{9}}}
\end_inset
\end_layout
\begin_layout Standard
Note that the use of the exponential map in local coordinate mappings is
not obligatory, even in the context of Lie groups.
Often it is computationally expedient to use mappings that are easier to
compute, but yet induce the same tangent vector at
\begin_inset Formula $T_{0}.$
\end_inset
Mappings that satisfy this constraint are collectively known as
\series bold
retractions
\series default
.
For example, for
\begin_inset Formula $\SEthree$
\end_inset
one could use the retraction
\begin_inset Formula $\mathcal{R}_{T_{0}}:\Rsix\rightarrow\SEthree$
\end_inset
\begin_inset Formula
\[
\mathcal{R}_{T_{0}}\left(\xi\right)=T_{0}\left\{ \exp\left(\Skew{\omega t}\right),vt\right\} =\left\{ \Phi_{R_{0}}\left(\omega t\right),P_{0}+R_{0}vt\right\}
\]
\end_inset
This trajectory describes a linear path in position while the frame rotates,
as opposed to the helical path traced out by the exponential map.
The tangent vector at
\begin_inset Formula $T_{0}$
\end_inset
can be computed as
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
\frac{d\mathcal{R}_{T_{0}}\left(\xi\right)}{dt}\biggr\vert_{t=0}=\left[R_{0}\Skew{\omega},R_{0}v\right]
\]
\end_inset
which is identical to the one induced by
\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 $\Phi_{T_{0}}(\xi)=T_{0}\exp\xihat$
\end_inset
.
\end_layout
\begin_layout Standard
The NavState manifold is not a Lie group like
\begin_inset Formula $\SEthree$
\end_inset
, but we can easily define a retraction that behaves similarly to the one
for
\begin_inset Formula $\SEthree$
\end_inset
, while treating velocities the same way as positions:
\begin_inset Formula
\[
\mathcal{R}_{X_{0}}(\zeta)=\left\{ \Phi_{R_{0}}\left(\omega t\right),P_{0}+R_{0}vt,V_{0}+R_{0}at\right\}
\]
\end_inset
Here
\begin_inset Formula $\zeta=\left[\omega t,vt,at\right]$
\end_inset
is a 9-vector, with respectively angular, position, and velocity components.
The tangent vector at
\begin_inset Formula $X_{0}$
\end_inset
is
\begin_inset Formula
\[
\frac{d\mathcal{R}_{X_{0}}(\zeta)}{dt}\biggr\vert_{t=0}=\left[R_{0}\Skew{\omega},R_{0}v,R_{0}a\right]
\]
\end_inset
and the isomorphism between
\begin_inset Formula $\Rnine$
\end_inset
and
\begin_inset Formula $T_{X_{0}}M$
\end_inset
is
\begin_inset Formula $\zeta\rightarrow\left[R_{0}\Skew{\omega t},R_{0}vt,R_{0}at\right]$
\end_inset
.
\end_layout
\begin_layout Subsubsection*
Integration in Local Coordinates
\end_layout
\begin_layout Standard
We now proceed exactly as before to describe the evolution of the NavState
in local coordinates.
Let us model the solution of the differential equation
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:diffeqM"
\end_inset
as a trajectory
\begin_inset Formula $\zeta(t)=\left[\theta(t),p(t),v(t)\right]$
\end_inset
, with
\begin_inset Formula $\zeta(0)=0$
\end_inset
, in the local coordinate frame anchored at
\begin_inset Formula $X_{0}$
\end_inset
.
Note that this trajectory evolves away from
\begin_inset Formula $X_{0}$
\end_inset
, and we use the symbols
\begin_inset Formula $\theta$
\end_inset
,
\begin_inset Formula $p$
\end_inset
, and
\begin_inset Formula $v$
\end_inset
to indicate that these are integrated rather than differential quantities.
With that, we have
\begin_inset Formula
\begin{equation}
X(t)=\mathcal{R}_{X_{0}}(\zeta(t))=\left\{ \Phi_{R_{0}}\left(\theta(t)\right),P_{0}+R_{0}p(t),V_{0}+R_{0}v(t)\right\} \label{eq:scheme1}
\end{equation}
\end_inset
We can create a trajectory
\begin_inset Formula $\gamma(\delta)$
\end_inset
that passes through
\begin_inset Formula $X(t)$
\end_inset
for
\begin_inset Formula $\delta=0$
\end_inset
\begin_inset Formula
\[
\gamma(\delta)=X(t+\delta)=\left\{ \Phi_{R_{0}}\left(\theta(t)+\dot{\theta}(t)\delta\right),P_{0}+R_{0}\left\{ p(t)+\dot{p}(t)\delta\right\} ,V_{0}+R_{0}\left\{ v(t)+\dot{v}(t)\delta\right\} \right\}
\]
\end_inset
and taking the derivative for
\begin_inset Formula $\delta=0$
\end_inset
we obtain
\begin_inset Formula
\[
\dot{X}(t)=\frac{d\gamma(\delta)}{d\delta}\biggr\vert_{\delta=0}=\left[R(t)\Skew{H(\theta)\dot{\theta}(t)},R_{0}\,\dot{p}(t),R_{0}\,\dot{v}(t)\right]
\]
\end_inset
Comparing that with the vector field
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:bodyField"
\end_inset
, we have exact integration iff
\begin_inset Formula
\[
\left[R(t)\Skew{H(\theta)\dot{\theta}(t)},R_{0}\,\dot{p}(t),R_{0}\,\dot{v}(t)\right]=\left[R(t)\Skew{\omega^{b}(t)},V(t),g+R(t)a^{b}(t)\right]
\]
\end_inset
Or, as another way to state this, if we solve the differential equations
for
\begin_inset Formula $\theta(t)$
\end_inset
,
\begin_inset Formula $p(t)$
\end_inset
, and
\begin_inset Formula $v(t)$
\end_inset
such that
\begin_inset Formula
\begin{eqnarray*}
\dot{\theta}(t) & = & H(\theta)^{-1}\,\omega^{b}(t)\\
\dot{p}(t) & = & R_{0}^{T}\,V_{0}+v(t)\\
\dot{v}(t) & = & R_{0}^{T}\,g+R_{b}^{0}(t)a^{b}(t)
\end{eqnarray*}
\end_inset
where
\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 $R_{b}^{0}(t)=R_{0}^{T}R(t)$
\end_inset
is the rotation of the body frame with respect to
\begin_inset Formula $R_{0}$
\end_inset
, and we have used
\begin_inset Formula $V(t)=V_{0}+R_{0}v(t)$
\end_inset
.
\end_layout
\begin_layout Subsubsection*
Application: The New IMU Factor
\end_layout
\begin_layout Standard
In the IMU factor, we need to predict the NavState
\begin_inset Formula $X_{j}$
\end_inset
from the current NavState
\begin_inset Formula $X_{i}$
\end_inset
and the IMU measurements in-between.
The above scheme suffers from a problem, which is that
\begin_inset Formula $X_{i}$
\end_inset
needs to be known in order to compensate properly for the initial velocity
and rotated gravity vector.
Hence, the idea of Lupton was to split up
\begin_inset Formula $v(t)$
\end_inset
into a gravity-induced part and an accelerometer part
\begin_inset Formula
\[
v(t)=v_{g}(t)+v_{a}(t)
\]
\end_inset
evolving as
\begin_inset Formula
\begin{eqnarray*}
\dot{v}_{g}(t) & = & R_{i}^{T}\,g\\
\dot{v}_{a}(t) & = & R_{b}^{i}(t)a^{b}(t)
\end{eqnarray*}
\end_inset
The solution for the first equation is simply
\begin_inset Formula $v_{g}(t)=R_{i}^{T}gt$
\end_inset
.
Similarly, we split the position
\begin_inset Formula $p(t)$
\end_inset
up in three parts
\begin_inset Formula
\[
p(t)=p_{i}(t)+p_{g}(t)+p_{v}(t)
\]
\end_inset
evolving as
\begin_inset Formula
\begin{eqnarray*}
\dot{p}_{i}(t) & = & R_{i}^{T}\,V_{i}\\
\dot{p}_{g}(t) & = & v_{g}(t)=R_{i}^{T}gt\\
\dot{p}_{v}(t) & = & v_{a}(t)
\end{eqnarray*}
\end_inset
Here the solutions for the two first equations are simply
\begin_inset Formula
\begin{eqnarray*}
p_{i}(t) & = & R_{i}^{T}V_{i}t\\
p_{g}(t) & = & R_{i}^{T}\frac{gt^{2}}{2}
\end{eqnarray*}
\end_inset
The recipe for the IMU factor is then, in summary.
Solve the ordinary differential equations
\begin_inset Formula
\begin{eqnarray*}
\dot{\theta}(t) & = & H(\theta(t))^{-1}\,\omega^{b}(t)\\
\dot{p}_{v}(t) & = & v_{a}(t)\\
\dot{v}_{a}(t) & = & R_{b}^{i}(t)a^{b}(t)
\end{eqnarray*}
\end_inset
starting from zero, up to time
\begin_inset Formula $t_{ij}$
\end_inset
, where
\begin_inset Formula $R_{b}^{i}(t)=\exp\Skew{\theta(t)}$
\end_inset
at all times.
Form the local coordinate vector as
\begin_inset Formula
\[
\zeta(t_{ij})=\left[\theta(t_{ij}),p(t_{ij}),v(t_{ij})\right]=\left[\theta(t_{ij}),R_{i}^{T}V_{i}t_{ij}+R_{i}^{T}\frac{gt_{ij}^{2}}{2}+p_{v}(t_{ij}),R_{i}^{T}gt_{ij}+v_{a}(t_{ij})\right]
\]
\end_inset
Predict the NavState
\begin_inset Formula $X_{j}$
\end_inset
at time
\begin_inset Formula $t_{j}$
\end_inset
from
\begin_inset Formula
\[
X_{j}=\mathcal{R}_{X_{i}}(\zeta(t_{ij}))=\left\{ \Phi_{R_{0}}\left(\theta(t_{ij})\right),P_{i}+V_{i}t_{ij}+\frac{gt_{ij}^{2}}{2}+R_{i}\,p_{v}(t_{ij}),V_{i}+gt_{ij}+R_{i}\,v_{a}(t_{ij})\right\}
\]
\end_inset
\end_layout
\begin_layout Standard
Note that the predicted NavState
\begin_inset Formula $X_{j}$
\end_inset
depends on
\begin_inset Formula $X_{i}$
\end_inset
, but the integrated quantities
\begin_inset Formula $\theta(t)$
\end_inset
,
\begin_inset Formula $p_{v}(t)$
\end_inset
, and
\begin_inset Formula $v_{a}(t)$
\end_inset
do not.
\end_layout
\begin_layout Subsubsection*
A Simple Euler Scheme
\end_layout
\begin_layout Standard
To solve the differential equation we can use a simple Euler scheme:
\begin_inset Formula
\begin{eqnarray}
\theta_{k+1}=\theta_{k}+\dot{\theta}(t_{k})\Delta_{t} & = & \theta_{k}+H(\theta_{k})^{-1}\,\omega_{k}^{b}\Delta_{t}\label{eq:euler_theta-1}\\
p_{k+1}=p_{k}+\dot{p}_{v}(t_{k})\Delta_{t} & = & p_{k}+v_{k}\Delta_{t}\label{eq:euler_p-1}\\
v_{k+1}=v_{k}+\dot{v}_{a}(t_{k})\Delta_{t} & = & v_{k}+\exp\left(\Skew{\theta_{k}}\right)a_{k}^{b}\Delta_{t}\label{eq:euler_v-1}
\end{eqnarray}
\end_inset
where
\begin_inset Formula $\theta_{k}\define\theta(t_{k})$
\end_inset
,
\begin_inset Formula $p_{k}\define p_{v}(t_{k})$
\end_inset
, and
\begin_inset Formula $v_{k}\define v_{a}(t_{k})$
\end_inset
.
However, the position propagation can be done more accurately, by using
exact integration of the zero-order hold acceleration
\begin_inset Formula $a_{k}^{b}$
\end_inset
:
\begin_inset Formula
\begin{eqnarray}
\theta_{k+1} & = & \theta_{k}+H(\theta_{k})^{-1}\,\omega_{k}^{b}\Delta_{t}\label{eq:euler_theta}\\
p_{k+1} & = & p_{k}+v_{k}\Delta_{t}+R_{k}a_{k}^{b}\frac{\Delta_{t}^{2}}{2}\label{eq:euler_p}\\
v_{k+1} & = & v_{k}+R_{k}a_{k}^{b}\Delta_{t}\label{eq:euler_v}
\end{eqnarray}
\end_inset
where we defined the rotation matrix
\begin_inset Formula $R_{k}=\exp\left(\Skew{\theta_{k}}\right)$
\end_inset
.
\end_layout
\begin_layout Subsubsection*
Noise Propagation
\end_layout
\begin_layout Standard
Even when we assume uncorrelated noise on
\begin_inset Formula $\omega^{b}$
\end_inset
and
\begin_inset Formula $a^{b}$
\end_inset
, the noise on the final computed quantities will have a non-trivial covariance
structure, because the intermediate quantities
\begin_inset Formula $\theta_{k}$
\end_inset
and
\begin_inset Formula $v_{k}$
\end_inset
appear in multiple places.
To model the noise propagation, let us define
\begin_inset Formula $\zeta_{k}=[\theta_{k},p_{k},v_{k}]$
\end_inset
and rewrite Eqns.
(
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:euler_theta"
\end_inset
-
\begin_inset CommandInset ref
LatexCommand ref
reference "eq:euler_v"
\end_inset
) as the non-linear function
\begin_inset Formula $f$
\end_inset
\begin_inset Formula
\[
\zeta_{k+1}=f\left(\zeta_{k},a_{k}^{b},\omega_{k}^{b}\right)
\]
\end_inset
Then the noise on
\begin_inset Formula $\zeta_{k+1}$
\end_inset
propagates as
\begin_inset Formula
\begin{equation}
\Sigma_{k+1}=A_{k}\Sigma_{k}A_{k}^{T}+B_{k}\Sigma_{\eta}^{ad}B_{k}+C_{k}\Sigma_{\eta}^{gd}C_{k}\label{eq:prop}
\end{equation}
\end_inset
where
\begin_inset Formula $A_{k}$
\end_inset
is the
\begin_inset Formula $9\times9$
\end_inset
partial derivative of
\begin_inset Formula $f$
\end_inset
wrpt
\begin_inset Formula $\zeta$
\end_inset
, and
\begin_inset Formula $B_{k}$
\end_inset
and
\begin_inset Formula $C_{k}$
\end_inset
the respective
\begin_inset Formula $9\times3$
\end_inset
partial derivatives with respect to the measured quantities
\begin_inset Formula $a^{b}$
\end_inset
and
\begin_inset Formula $\omega^{b}$
\end_inset
.
\end_layout
\begin_layout Standard
We start with the noise propagation on
\begin_inset Formula $\theta$
\end_inset
, which is independent of the other quantities.
Taking the derivative, we have
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
\deriv{\theta_{k+1}}{\theta_{k}}=I_{3x3}+\deriv{H(\theta_{k})^{-1}\omega_{k}^{b}}{\theta_{k}}\Delta_{t}
\]
\end_inset
It can be shown that for small
\begin_inset Formula $\theta_{k}$
\end_inset
we have
\begin_inset Formula
\[
\deriv{H(\theta_{k})^{-1}\omega_{k}^{b}}{\theta_{k}}\approx-\frac{1}{2}\Skew{\omega_{k}^{b}}\mbox{ and hence }\deriv{\theta_{k+1}}{\theta_{k}}=I_{3x3}-\frac{\Delta t}{2}\Skew{\omega_{k}^{b}}
\]
\end_inset
For the derivatives of
\begin_inset Formula $p_{k+1}$
\end_inset
and
\begin_inset Formula $v_{k+1}$
\end_inset
we need the derivative
\begin_inset Formula
\[
\deriv{R_{k}a_{k}^{b}}{\theta_{k}}=R_{k}\Skew{-a_{k}^{b}}\deriv{R_{k}}{\theta_{k}}=R_{k}\Skew{-a_{k}^{b}}H(\theta_{k})
\]
\end_inset
where we used
\begin_inset Formula
\[
\deriv{\left(Ra\right)}R\approx R\Skew{-a}
\]
\end_inset
and the fact that the dependence of the rotation
\begin_inset Formula $R_{k}$
\end_inset
on
\begin_inset Formula $\theta_{k}$
\end_inset
is the already computed
\begin_inset Formula $H(\theta_{k})$
\end_inset
.
\end_layout
\begin_layout Standard
Putting all this together, we finally obtain
\begin_inset Formula
\[
A_{k}\approx\left[\begin{array}{ccc}
I_{3\times3}-\frac{\Delta_{t}}{2}\Skew{\omega_{k}^{b}}\\
R_{k}\Skew{-a_{k}^{b}}H(\theta_{k})\frac{\Delta_{t}}{2}^{2} & I_{3\times3} & I_{3\times3}\Delta_{t}\\
R_{k}\Skew{-a_{k}^{b}}H(\theta_{k})\Delta_{t} & & I_{3\times3}
\end{array}\right]
\]
\end_inset
The other partial derivatives are simply
\begin_inset Formula
\[
B_{k}=\left[\begin{array}{c}
0_{3\times3}\\
R_{k}\frac{\Delta_{t}}{2}^{2}\\
R_{k}\Delta_{t}
\end{array}\right],\,\,\,\,C_{k}=\left[\begin{array}{c}
H(\theta_{k})^{-1}\Delta_{t}\\
0_{3\times3}\\
0_{3\times3}
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Subsubsection*
Combined IMU Factor
\end_layout
\begin_layout Standard
We can similarly account for bias drift over time, as is commonly seen in
commercial grade IMUs.
This is accomplished via the
\emph on
CombinedImuFactor
\emph default
which is a 6-way factor between the previous
\emph on
pose/velocity/bias
\emph default
and the
\emph on
pose/velocity/bias
\emph default
at the next timestep.
\end_layout
\begin_layout Standard
We expand the state vector as
\begin_inset Formula $\zeta_{k}=[\theta_{k},p_{k},v_{k},b_{k}^{a},b_{k}^{\omega}]$
\end_inset
to include the bias terms.
This gives the noise propagation equation as
\end_layout
\begin_layout Standard
\begin_inset Formula
\begin{equation}
\Sigma_{k+1}=F_{k}\Sigma_{k}F_{k}^{T}+G_{k}\Sigma_{k}G_{k}\label{eq:prop-combined}
\end{equation}
\end_inset
\end_layout
\begin_layout Standard
where
\begin_inset Formula $F_{k}$
\end_inset
is the
\begin_inset Formula $15\times15$
\end_inset
derivative of
\begin_inset Formula $f$
\end_inset
wrpt this new
\begin_inset Formula $\zeta$
\end_inset
, and
\begin_inset Formula $G_{k}$
\end_inset
is the
\begin_inset Formula $15\times15$
\end_inset
matrix for first order uncertainty propagation.
The top-left
\begin_inset Formula $9\times9$
\end_inset
of
\begin_inset Formula $F_{k}$
\end_inset
is the same as
\begin_inset Formula $A_{k}$
\end_inset
, thus we only have the jacobians wrpt the biases left to account for.
\end_layout
\begin_layout Standard
Conveniently, the jacobians of the pose and velocity wrpt the biases are
already computed in the
\emph on
ImuFactor
\emph default
derivation as matrices
\begin_inset Formula $B_{k}$
\end_inset
and
\begin_inset Formula $C_{k}$
\end_inset
, while they are identity matrices wrpt the biases themselves.
Thus, we can easily plug-in the values from the previous section to give
us the final result
\end_layout
\begin_layout Standard
\begin_inset Formula
\[
F_{k}\approx\left[\begin{array}{ccccc}
I_{3\times3}-\frac{\Delta_{t}}{2}\Skew{\omega_{k}^{b}} & & & & H(\theta_{k})^{-1}\Delta_{t}\\
R_{k}\Skew{-a_{k}^{b}}H(\theta_{k})\frac{\Delta_{t}}{2}^{2} & I_{3\times3} & I_{3\times3}\Delta_{t} & R_{k}\frac{\Delta_{t}}{2}^{2}\\
R_{k}\Skew{-a_{k}^{b}}H(\theta_{k})\Delta_{t} & & I_{3\times3} & R_{k}\Delta_{t}\\
& & & I_{3\times3}\\
& & & & I_{3\times3}
\end{array}\right]
\]
\end_inset
\end_layout
\begin_layout Standard
\begin_inset CommandInset bibtex
LatexCommand bibtex
bibfiles "refs"
options "plain"
\end_inset
\end_layout
\end_body
\end_document