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22 #include <Eigen/Dense>
35 using FunctionEvaluation = std::function<std::tuple<bool, bfl::Data, bfl::VectorDescription>(
const Eigen::Ref<const Eigen::MatrixXd>&)>;
51 UTWeight(std::size_t dof,
const double alpha,
const double beta,
const double kappa);
60 void unscented_weights(
const std::size_t n,
const double alpha,
const double beta,
const double kappa, Eigen::Ref<Eigen::VectorXd> weight_mean, Eigen::Ref<Eigen::VectorXd> weight_covariance,
double& c);
double c
c = sqrt(n + lambda) with lambda a ut parameter.
Port of boost::any for C++11 compilers.
Eigen::MatrixXd sigma_point(const GaussianMixture &state, const double c)
std::tuple< bool, GaussianMixture, Eigen::MatrixXd > unscented_transform(const GaussianMixture &input, const UTWeight &weight, FunctionEvaluation function)
UTWeight(std::size_t dof, const double alpha, const double beta, const double kappa)
Constructs the weights from number of degrees of freedom of the input space and UT parameters alpha,...
This class represent an additive measurement model f(x) + w, where x is a state vector and w is rando...
This class represent a generic measurement model f(x, w), where x is a state vector and w is random n...
void unscented_weights(const std::size_t n, const double alpha, const double beta, const double kappa, Eigen::Ref< Eigen::VectorXd > weight_mean, Eigen::Ref< Eigen::VectorXd > weight_covariance, double &c)
Eigen::VectorXd covariance
std::function< std::tuple< bool, bfl::Data, bfl::VectorDescription >(const Eigen::Ref< const Eigen::MatrixXd > &)> FunctionEvaluation
A FunctionEvaluation return.