/// <summary>
/// Convert a partially-initialised feature to a fully-initialised feature,
/// given information about the free parameters \vct{\lambda}.
/// The new state \vct{y}_{fi} is given by calling
/// Partially_Initialised_Feature_Measurement_Model::func_yfi_and_dyfi_by_dypi_and_dyfi_by_dlambda().
/// where the various Jacobians are returned by calls to
/// Partially_Initialised_Feature_Measurement_Model, and the covariance matrices
/// \mat{P}_{kl} are already known and stored in the class, except for
/// \mat{P}_{\vct{\lambda}}, which is passed to the function.
/// </summary>
/// <param name="lambda">The mean value for \vct{\lambda}</param>
/// <param name="Plambda">The covariance for \vct{\lambda}</param>
/// <param name="scene">The SLAM map</param>
public void convert_from_partially_to_fully_initialised(
Vector lambda, MatrixFixed Plambda, Scene_Single scene)
{
// We'll do all the work here in feature.cc though probably this only
// works with scene_single...
// We calculate new state yfi(ypi, lambda)
// New feature covariance
// Pyfiyfi = dyfi_by_dypi Pypiypi dyfi_by_dypiT +
// dyfi_by_dlambda Plambda dyfi_by_dlambdaT
// And we change cross covariances as follows:
// Pxyfi = Pxypi dyfi_by_dypiT
// Pyjyfi = Pyjypi dyfi_by_dypiT for j < i (since we only store top-right
// Pyfiyj = dyfi_by_dypi Pypiyj for j > i part of covariance matrix)
partially_initialised_feature_measurement_model.func_yfi_and_dyfi_by_dypi_and_dyfi_by_dlambda(y, lambda);
MatrixFixed dyfi_by_dypiT = partially_initialised_feature_measurement_model.get_dyfi_by_dypiRES().Transpose();
MatrixFixed dyfi_by_dlambdaT = partially_initialised_feature_measurement_model.get_dyfi_by_dlambdaRES().Transpose();
// Replace y
y = new Vector(partially_initialised_feature_measurement_model.get_yfiRES());
// Replace Pxy
Pxy = Pxy * dyfi_by_dypiT;
// Replace Pyy
MatrixFixed Pypiypi_1 = partially_initialised_feature_measurement_model.get_dyfi_by_dypiRES() *
Pyy * dyfi_by_dypiT;
MatrixFixed Pypiypi_2 = partially_initialised_feature_measurement_model.get_dyfi_by_dlambdaRES() *
Plambda * dyfi_by_dlambdaT;
Pyy = Pypiypi_1 + Pypiypi_2;
// Pyjyi elements for j < i (covariance with features before i in list)
uint i = position_in_list;
MatrixFixed m_it;
int j;
for (j = 0; j < position_in_list; j++)
{
m_it = (MatrixFixed)matrix_block_list[j];
matrix_block_list[j] = m_it * dyfi_by_dypiT;
}
Feature it;
int idx = scene.feature_list.IndexOf(this);
for (j = idx + 1; j < scene.feature_list.Count; j++)
{
it = (Feature)(scene.feature_list[j]);
it.matrix_block_list[(int)i] = partially_initialised_feature_measurement_model.get_dyfi_by_dypiRES() * (MatrixFixed)it.matrix_block_list[(int)i];
it.increment_position_in_total_state_vector(-(int)feature_measurement_model.FEATURE_STATE_SIZE);
}
// Change the total state size in scene, here with a negative increment
uint size1 = partially_initialised_feature_measurement_model.more_initialised_feature_measurement_model.FEATURE_STATE_SIZE;
uint size2 = partially_initialised_feature_measurement_model.FEATURE_STATE_SIZE;
scene.increment_total_state_size((int)size1 - (int)size2);
// Change fmm for this model to fully-initialised one
feature_measurement_model =
partially_initialised_feature_measurement_model.more_initialised_feature_measurement_model;
partially_initialised_feature_measurement_model = null;
fully_initialised_feature_measurement_model =
(Fully_Initialised_Feature_Measurement_Model)feature_measurement_model;
//assert(fully_initialised_feature_measurement_model != NULL);
// Need to reallocate any other matrices
// Assume that measurement size doesn't change
dh_by_dy.Resize(feature_measurement_model.MEASUREMENT_SIZE, feature_measurement_model.FEATURE_STATE_SIZE);
}
