UpdaterHelper class
Class that has helper functions for our updaters.
Contents
- Reference
Can compute the Jacobian for a single feature representation. This will create the Jacobian based on what representation our state is in. If we are using the anchor representation then we also have additional Jacobians in respect to the anchor state. Also has functions such as nullspace projection and full jacobian construction. For derivations look at Camera Measurement Update page which has detailed equations.
Public types
- struct UpdaterHelperFeature
- Feature object that our UpdaterHelper leverages, has all measurements and means.
Public static functions
- static void get_feature_jacobian_representation(State* state, UpdaterHelperFeature& feature, Eigen::Matrix<double, 3, 3>& H_f, std::vector<Eigen::Matrix<double, 3, Eigen::Dynamic>>& H_x, std::vector<Type*>& x_order)
- This gets the feature and state Jacobian in respect to the feature representation.
- static void get_feature_jacobian_intrinsics(State* state, const Eigen::Vector2d& uv_norm, bool isfisheye, Eigen::Matrix<double, 8, 1> cam_d, Eigen::Matrix<double, 2, 2>& dz_dzn, Eigen::Matrix<double, 2, 8>& dz_dzeta)
- This will compute the Jacobian in respect to the intrinsic calibration parameters and normalized coordinates.
- static void get_feature_jacobian_full(State* state, UpdaterHelperFeature& feature, Eigen::MatrixXd& H_f, Eigen::MatrixXd& H_x, Eigen::VectorXd& res, std::vector<Type*>& x_order)
- Will construct the "stacked" Jacobians for a single feature from all its measurements.
- static void nullspace_project_inplace(Eigen::MatrixXd& H_f, Eigen::MatrixXd& H_x, Eigen::VectorXd& res)
- This will project the left nullspace of H_f onto the linear system.
- static void measurement_compress_inplace(Eigen::MatrixXd& H_x, Eigen::VectorXd& res)
- This will perform measurement compression.
Function documentation
static void ov_msckf::
This gets the feature and state Jacobian in respect to the feature representation.
| Parameters | |
|---|---|
| state in | State of the filter system |
| feature in | Feature we want to get Jacobians of (must have feature means) |
| H_f out | Jacobians in respect to the feature error state |
| H_x out | Extra Jacobians in respect to the state (for example anchored pose) |
| x_order out | Extra variables our extra Jacobian has (for example anchored pose) |
static void ov_msckf::
This will compute the Jacobian in respect to the intrinsic calibration parameters and normalized coordinates.
| Parameters | |
|---|---|
| state | State of the filter system |
| uv_norm | Normalized image coordinates |
| isfisheye | If this feature is for a fisheye model |
| cam_d | Camera intrinsics values |
| dz_dzn | Derivative in respect to normalized coordinates |
| dz_dzeta | Derivative in respect to distortion paramters |
static void ov_msckf::
Will construct the "stacked" Jacobians for a single feature from all its measurements.
| Parameters | |
|---|---|
| state in | State of the filter system |
| feature in | Feature we want to get Jacobians of (must have feature means) |
| H_f out | Jacobians in respect to the feature error state |
| H_x out | Extra Jacobians in respect to the state (for example anchored pose) |
| res out | Measurement residual for this feature |
| x_order out | Extra variables our extra Jacobian has (for example anchored pose) |
static void ov_msckf::
This will project the left nullspace of H_f onto the linear system.
| Parameters | |
|---|---|
| H_f | Jacobian with nullspace we want to project onto the system [res = Hx*(x-xhat)+Hf(f-fhat)+n] |
| H_x | State jacobian |
| res | Measurement residual |
Please see the MSCKF Nullspace Projection for details on how this works. This is the MSCKF nullspace projection which removes the dependency on the feature state. Note that this is done in place so all matrices will be different after a function call.
static void ov_msckf::
This will perform measurement compression.
| Parameters | |
|---|---|
| H_x | State jacobian |
| res | Measurement residual |
Please see the Measurement Compression for details on how this works. Note that this is done in place so all matrices will be different after a function call.