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