lyx update
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@ -227,16 +227,62 @@ preintegrated_
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\begin_layout Standard
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\begin_layout Standard
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The main function of a factor is to calculate an error.
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The main function of a factor is to calculate an error.
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The easiest case to look at is the NavState variant in ImuFactor2, which
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This is done exactly the same in both variants:
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is given as:
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\begin_inset Formula
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\begin_inset Formula
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\begin{equation}
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\begin{equation}
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\Delta X_{ij}=X_{j}-\hat{X_{ij}}\label{eq:imu-factor-error}
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e(X_{i},X_{j})=X_{j}\ominus\widehat{X_{j}}\label{eq:imu-factor-error}
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\end{equation}
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\end{equation}
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\end_inset
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\end_inset
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where the predicted NavState
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\begin_inset Formula $\widehat{X_{j}}$
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\end_inset
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at time
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\begin_inset Formula $t_{j}$
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\end_inset
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is a function of the NavState
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\begin_inset Formula $X_{i}$
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\end_inset
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at time
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\begin_inset Formula $t_{i}$
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\end_inset
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and the preintegrated measurements
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\begin_inset Formula $PIM$
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\end_inset
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:
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\begin_inset Formula
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\[
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\widehat{X_{j}}=f(X_{i},PIM)
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\]
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\end_inset
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The noise model associated with this factor is assumed to be zero-mean Gaussian
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with a
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\begin_inset Formula $9\times9$
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\end_inset
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covariance matrix
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\begin_inset Formula $\Sigma_{ij}$
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\end_inset
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, which is defined in the tangent space
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\begin_inset Formula $T_{X_{j}}\mathcal{N}$
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\end_inset
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of the NavState manifold at the NavState
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\begin_inset Formula $X_{j}$
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\end_inset
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.
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This covariance matrix is computed in the preintegrated measurement class,
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of which there are two variants as discussed above.
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\end_layout
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\end_layout
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\begin_layout Subsubsection*
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\begin_layout Subsubsection*
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@ -282,6 +328,14 @@ Gyroscope Covariance
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: Measurement uncertainty of the gyroscope.
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: Measurement uncertainty of the gyroscope.
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\end_layout
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\end_layout
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\begin_layout Itemize
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Gyroscope Bias Covariance
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\begin_inset Formula $Q_{\Delta b^{\omega}}$
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\end_inset
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: The covariance associated with the gyroscope bias random walk.
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\end_layout
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\begin_layout Itemize
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\begin_layout Itemize
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Accelerometer Covariance
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Accelerometer Covariance
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\begin_inset Formula $Q_{acc}$
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\begin_inset Formula $Q_{acc}$
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@ -298,14 +352,6 @@ Accelerometer Bias Covariance
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: The covariance associated with the accelerometer bias random walk.
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: The covariance associated with the accelerometer bias random walk.
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\end_layout
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\end_layout
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\begin_layout Itemize
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Gyroscope Bias Covariance
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\begin_inset Formula $Q_{\Delta b^{\omega}}$
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\end_inset
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: The covariance associated with the gyroscope bias random walk.
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\end_layout
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\begin_layout Itemize
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\begin_layout Itemize
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Integration Covariance
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Integration Covariance
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\begin_inset Formula $Q_{int}$
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\begin_inset Formula $Q_{int}$
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@ -1469,7 +1515,12 @@ Noise Propagation in IMU Factor
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\end_layout
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\end_layout
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\begin_layout Standard
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\begin_layout Standard
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Even when we assume uncorrelated noise on
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We wish to compute the ImuFactor covariance matrix
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\begin_inset Formula $\Sigma_{ij}$
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\end_inset
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.
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Even when we assume uncorrelated noise on
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\begin_inset Formula $\omega^{b}$
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\begin_inset Formula $\omega^{b}$
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\end_inset
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\end_inset
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@ -1487,11 +1538,12 @@ Even when we assume uncorrelated noise on
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\end_inset
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\end_inset
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appear in multiple places.
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appear in multiple places.
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To model the noise propagation, let us define
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To model the noise propagation, let us define the preintegrated navigation
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state
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\begin_inset Formula $\zeta_{k}=[\theta_{k},p_{k},v_{k}]$
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\begin_inset Formula $\zeta_{k}=[\theta_{k},p_{k},v_{k}]$
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\end_inset
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\end_inset
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and rewrite Eqns.
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, as a 9D vector on tangent space at and rewrite Eqns.
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(
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(
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\begin_inset CommandInset ref
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\begin_inset CommandInset ref
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LatexCommand ref
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LatexCommand ref
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