Address some pr comments - remove ABC_EQF.cpp, clean up comments, change Input class to struct, remove changes to target
jenniferoum
parent
d48bf56ff8
commit
e5f4978539
1009
examples/ABC_EQF.cpp
1009
examples/ABC_EQF.cpp
File diff suppressed because it is too large
Load Diff
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@ -46,38 +46,33 @@ extern const std::string COORDINATE;
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// Utility Functions
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//========================================================================
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/**
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* Check if a vector is a unit vector
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*/
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/// Check if a vector is a unit vector
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bool checkNorm(const Vector3& x, double tol = 1e-3);
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/**
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* Check if vector contains NaN values
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*/
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/// Check if vector contains NaN values
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bool hasNaN(const Vector3& vec);
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/**
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* Create a block diagonal matrix from two matrices
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*/
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/// Create a block diagonal matrix from two matrices
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Matrix blockDiag(const Matrix& A, const Matrix& B);
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/**
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* Repeat a block matrix n times along the diagonal
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*/
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/// Repeat a block matrix n times along the diagonal
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Matrix repBlock(const Matrix& A, int n);
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/**
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* Calculate numerical differential
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*/
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/// Calculate numerical differential
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Matrix numericalDifferential(std::function<Vector(const Vector&)> f, const Vector& x);
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//========================================================================
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// Core Data Types
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//========================================================================
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/**
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* Direction class as a S2 element
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*/
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/// Direction class as a S2 element
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class Direction {
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public:
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Unit3 d; // Direction (unit vector on S2)
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@ -97,60 +92,42 @@ public:
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bool hasNaN() const;
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};
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/**
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* Input class for the Biased Attitude System
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*/
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class Input {
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public:
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Vector3 w; // Angular velocity
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Matrix Sigma; // Noise covariance
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/// Input class for the Biased Attitude System
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/**
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* Initialize Input
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* @param w Angular velocity (3-vector)
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* @param Sigma Noise covariance (6x6 matrix)
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*/
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Input(const Vector3& w, const Matrix& Sigma);
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struct Input {
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Vector3 w; /// Angular velocity (3-vector)
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Matrix Sigma; /// Noise covariance (6x6 matrix)
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static Input random(); /// Random input
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Matrix3 W() const { /// Return w as a skew symmetric matrix
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return Rot3::Hat(w);
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}
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static Input create(const Vector3& w, const Matrix& Sigma) { /// Initialize w and Sigma
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if (Sigma.rows() != 6 || Sigma.cols() != 6) {
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throw std::invalid_argument("Input measurement noise covariance must be 6x6");
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}
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/// Check positive semi-definite
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Eigen::SelfAdjointEigenSolver<Matrix> eigensolver(Sigma);
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if (eigensolver.eigenvalues().minCoeff() < -1e-10) {
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throw std::invalid_argument("Covariance matrix must be semi-positive definite");
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}
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/**
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* Return the Input as a skew-symmetric matrix
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* @return w as a skew-symmetric matrix
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*/
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Matrix3 W() const;
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/**
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* Return a random angular velocity
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* @return A random angular velocity as Input element
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*/
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static Input random();
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return Input{w, Sigma}; // use brace initialization
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}
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};
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/**
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* Measurement class
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* cal_idx is an index corresponding to the calibration related to the measurement
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* cal_idx = -1 indicates the measurement is from a calibrated sensor
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*/
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class Measurement {
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public:
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Direction y; // Measurement direction in sensor frame
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Direction d; // Known direction in global frame
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Matrix3 Sigma; // Covariance matrix of the measurement
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int cal_idx = -1; // Calibration index (-1 for calibrated sensor)
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/// Measurement class
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/**
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* Initialize measurement
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* @param y_vec Direction measurement in sensor frame
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* @param d_vec Known direction in global frame
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* @param Sigma Measurement noise covariance
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* @param i Calibration index (-1 for calibrated sensor)
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*/
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struct Measurement {
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Direction y; /// Measurement direction in sensor frame
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Direction d; /// Known direction in global frame
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Matrix3 Sigma; /// Covariance matrix of the measurement
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int cal_idx = -1; /// Calibration index (-1 for calibrated sensor)
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Measurement(const Vector3& y_vec, const Vector3& d_vec,
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const Matrix3& Sigma, int i = -1);
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};
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/**
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* State class representing the state of the Biased Attitude System
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*/
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/// State class representing the state of the Biased Attitude System
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class State {
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public:
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Rot3 R; // Attitude rotation matrix R
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@ -164,9 +141,8 @@ public:
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static State identity(int n);
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};
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/**
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* Data structure for ground-truth, input and output data
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*/
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/// Data structure for ground-truth, input and output data
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struct Data {
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State xi; // Ground-truth state
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int n_cal; // Number of calibration states
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@ -305,25 +281,24 @@ Matrix stateActionDiff(const State& xi);
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// Equivariant Filter (EqF)
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//========================================================================
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/**
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* Equivariant Filter (EqF) implementation
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*/
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/// Equivariant Filter (EqF) implementation
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class EqF {
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private:
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int __dof; // Degrees of freedom
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int __n_cal; // Number of calibration states
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G __X_hat; // Filter state
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Matrix __Sigma; // Error covariance
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State __xi_0; // Origin state
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Matrix __Dphi0; // Differential of phi at origin
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Matrix __InnovationLift; // Innovation lift matrix
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int dof; // Degrees of freedom
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int n_cal; // Number of calibration states
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G X_hat; // Filter state
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Matrix Sigma; // Error covariance
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State xi_0; // Origin state
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Matrix Dphi0; // Differential of phi at origin
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Matrix InnovationLift; // Innovation lift matrix
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/**
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* Return the state matrix A0t (Equation 14a)
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* @param u Input
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* @return State matrix A0t
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*/
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Matrix __stateMatrixA(const Input& u) const;
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Matrix stateMatrixA(const Input& u) const;
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/**
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* Return the state transition matrix Phi (Equation 17)
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@ -331,13 +306,13 @@ private:
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* @param dt Time step
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* @return State transition matrix Phi
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*/
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Matrix __stateTransitionMatrix(const Input& u, double dt) const;
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Matrix stateTransitionMatrix(const Input& u, double dt) const;
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/**
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* Return the Input matrix Bt
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* @return Input matrix Bt
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*/
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Matrix __inputMatrixBt() const;
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Matrix inputMatrixBt() const;
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/**
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* Return the measurement matrix C0 (Equation 14b)
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@ -345,14 +320,14 @@ private:
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* @param idx Calibration index
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* @return Measurement matrix C0
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*/
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Matrix __measurementMatrixC(const Direction& d, int idx) const;
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Matrix measurementMatrixC(const Direction& d, int idx) const;
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/**
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* Return the measurement output matrix Dt
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* @param idx Calibration index
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* @return Measurement output matrix Dt
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*/
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Matrix __outputMatrixDt(int idx) const;
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Matrix outputMatrixDt(int idx) const;
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public:
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/**
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@ -383,43 +358,699 @@ public:
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void update(const Measurement& y);
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};
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// Global configuration
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const std::string COORDINATE = "EXPONENTIAL"; // Denotes how the states are mapped to the vector representations
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//========================================================================
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// Data Processing Functions
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// Utility Functions
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//========================================================================
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/**
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* @brief Verifies if a vector has unit norm within tolerance
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* @param x 3d vector
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* @param tol optional tolerance
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* @return Bool indicating that the vector norm is approximately 1
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* Uses Vector3 norm() method to calculate vector magnitude
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*/
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bool checkNorm(const Vector3& x, double tol) {
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return abs(x.norm() - 1) < tol || std::isnan(x.norm());
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}
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/**
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* @brief Checks if the input vector has any NaNs
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* @param vec A 3-D vector
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* @return true if present, false otherwise
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*/
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bool hasNaN(const Vector3& vec) {
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return std::isnan(vec[0]) || std::isnan(vec[1]) || std::isnan(vec[2]);
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}
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/**
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* @brief Creates a block diagonal matrix from input matrices
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* @param A Matrix A
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* @param B Matrix B
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* @return A single consolidated matrix with A in the top left and B in the
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* bottom right
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* Uses Matrix's rows(), cols(), setZero(), and block() methods
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*/
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Matrix blockDiag(const Matrix& A, const Matrix& B) {
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if (A.size() == 0) {
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return B;
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} else if (B.size() == 0) {
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return A;
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} else {
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Matrix result(A.rows() + B.rows(), A.cols() + B.cols());
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result.setZero();
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result.block(0, 0, A.rows(), A.cols()) = A;
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result.block(A.rows(), A.cols(), B.rows(), B.cols()) = B;
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return result;
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}
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}
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/**
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* @brief Creates a block diagonal matrix by repeating a matrix 'n' times
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* @param A A matrix
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* @param n Number of times to be repeated
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* @return Block diag matrix with A repeated 'n' times
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* Recursively uses blockDiag() function
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*/
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Matrix repBlock(const Matrix& A, int n) {
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if (n <= 0) return Matrix();
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Matrix result = A;
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for (int i = 1; i < n; i++) {
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result = blockDiag(result, A);
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}
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return result;
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}
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/**
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* @brief Calculates the Jacobian matrix using central difference approximation
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* @param f Vector function f
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* @param x The point at which Jacobian is evaluated
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* @return Matrix containing numerical partial derivatives of f at x
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* Uses Vector's size() and Zero(), Matrix's Zero() and col() methods
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*/
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Matrix numericalDifferential(std::function<Vector(const Vector&)> f, const Vector& x) {
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double h = 1e-6;
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Vector fx = f(x);
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int n = fx.size();
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int m = x.size();
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Matrix Df = Matrix::Zero(n, m);
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for (int j = 0; j < m; j++) {
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Vector ej = Vector::Zero(m);
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ej(j) = 1.0;
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Vector fplus = f(x + h * ej);
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Vector fminus = f(x - h * ej);
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Df.col(j) = (fplus - fminus) / (2*h);
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}
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return Df;
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}
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//========================================================================
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// Direction Class Implementation
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//========================================================================
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/**
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* Load data from CSV file into a vector of Data objects for the EqF
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*
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* CSV format:
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* - t: Time
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* - q_w, q_x, q_y, q_z: True attitude quaternion
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* - b_x, b_y, b_z: True bias
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* - cq_w_0, cq_x_0, cq_y_0, cq_z_0: True calibration quaternion
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* - w_x, w_y, w_z: Angular velocity measurements
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* - std_w_x, std_w_y, std_w_z: Angular velocity measurement standard deviations
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* - std_b_x, std_b_y, std_b_z: Bias process noise standard deviations
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* - y_x_0, y_y_0, y_z_0, y_x_1, y_y_1, y_z_1: Direction measurements
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* - std_y_x_0, std_y_y_0, std_y_z_0, std_y_x_1, std_y_y_1, std_y_z_1: Direction measurement standard deviations
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* - d_x_0, d_y_0, d_z_0, d_x_1, d_y_1, d_z_1: Reference directions
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*
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* @param filename Path to the CSV file
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* @param startRow First row to load (default: 0)
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* @param maxRows Maximum number of rows to load (default: all)
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* @param downsample Downsample factor (default: 1, which means no downsampling)
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* @return Vector of Data objects
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* @brief Initializes a direction object vector from a provided 3D vector input
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* @param d_vec A 3-D vector that should have a unit norm(This represents a
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* direction in 3D space) Uses Unit3's constructor which normalizes vectors
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*/
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std::vector<Data> loadDataFromCSV(const std::string& filename,
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int startRow = 0,
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int maxRows = -1,
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int downsample = 1);
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Direction::Direction(const Vector3& d_vec) : d(d_vec) {
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if (!checkNorm(d_vec)) {
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throw std::invalid_argument("Direction must be a unit vector");
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}
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}
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/** Access the individual components of the direction vector defined above using this methods below
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* Uses the Unit3 object from GTSAM to compute the components
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*/
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double Direction::x() const {
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return d.unitVector()[0];
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}
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double Direction::y() const {
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return d.unitVector()[1];
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}
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double Direction::z() const {
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return d.unitVector()[2];
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}
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bool Direction::hasNaN() const {
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Vector3 vec = d.unitVector();
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return std::isnan(vec[0]) || std::isnan(vec[1]) || std::isnan(vec[2]);
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}
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//========================================================================
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// Input Class Implementation
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//========================================================================
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/**
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* @brief Constructs an input object using the Angular velocity vector and the
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* covariance matrix
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* @param w Angular vector
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* @param Sigma 6X6 covariance matrix
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* Uses Matrix's rows(), cols() and Eigen's SelfAdjointEigenSolver
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*/
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// Input::Input(const Vector3& w, const Matrix& Sigma) : w(w), Sigma(Sigma) {
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// if (Sigma.rows() != 6 || Sigma.cols() != 6) {
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// throw std::invalid_argument("Input measurement noise covariance must be 6x6");
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// }
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//
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// // Check positive semi-definite
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// Eigen::SelfAdjointEigenSolver<Matrix> eigensolver(Sigma);
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// if (eigensolver.eigenvalues().minCoeff() < -1e-10) {
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// throw std::invalid_argument("Covariance matrix must be semi-positive definite");
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// }
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// }
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/**
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*
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* @return 3X3 skey symmetric matrix when called
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* Uses Rot3's Hat() to create skew-symmetric matrix
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*/
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// Matrix3 Input::W() const {
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// return Rot3::Hat(w);
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// }
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//========================================================================
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// Measurement Class Implementation
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//========================================================================
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/**
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* @brief Constructs measurement with directions and covariance.
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* @param y_vec A 3D vector representing the measured direction in the sensor frame
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* @param d_vec A 3D vector representing the known reference direction in the global frame aka ground truth direction
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* @param Sigma 3x3 positive definite covariance vector representing the uncertainty in the measurements
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* @param i Calibration index - A non-negative integer specifies the element in the calibration vector
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* that corresponds to the sensor of interest. A value of -1 indicates that all the sensors have been calibrated
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*
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* Creates a measurement object that stores the measured direction(y), reference direction(d), measurement noise covariance(Sigma)
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* and Calibration Index cal_idx
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*
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* Uses Eigen's SelfAdjointEigenSolver
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*
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*/
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Measurement::Measurement(const Vector3& y_vec, const Vector3& d_vec,
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const Matrix3& Sigma, int i)
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: y(y_vec), d(d_vec), Sigma(Sigma), cal_idx(i) {
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// Check positive semi-definite
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Eigen::SelfAdjointEigenSolver<Matrix3> eigensolver(Sigma);
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if (eigensolver.eigenvalues().minCoeff() < -1e-10) {
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throw std::invalid_argument("Covariance matrix must be semi-positive definite");
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}
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}
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//========================================================================
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// State Class Implementation
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//========================================================================
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/**
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*
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* @param R Rot3 (Attitude)
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* @param b Vector (Bias)
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* @param S Vector (Rot 3 calibration states)
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* Combines the navigation and the calibration states together and provides a
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* mechanism to represent the complete system
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*
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*/
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State::State(const Rot3& R, const Vector3& b, const std::vector<Rot3>& S)
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: R(R), b(b), S(S) {}
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/**
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* Process data with EqF and print summary results
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* @param filter Initialized EqF filter
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* @param data_list Vector of Data objects to process
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* @param printInterval Progress indicator interval (used internally)
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*
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* @param n Number of Calibration states
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* @return State object intitialized to identity
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* Creates a default/ initial state
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* Uses GTSAM's Rot3::identity and Vector3 zero function
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*/
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void processDataWithEqF(EqF& filter, const std::vector<Data>& data_list, int printInterval = 10);
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State State::identity(int n) {
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std::vector<Rot3> calibrations(n, Rot3::Identity());
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return State(Rot3::Identity(), Vector3::Zero(), calibrations);
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}
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//========================================================================
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// Data Struct Implementation
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//========================================================================
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/**
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*
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* @param xi Ground Truth state
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* @param n_cal Number of calibration states
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* @param u Input measurements
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* @param y Vector of y measurements
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* @param n_meas number of measurements
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* @param t timestamp
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* @param dt time step
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* Used to organize the state, input and measurement data with timestamps for
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* testing Uses Rot3, Vector 3 and Unit3 classes
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*/
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Data::Data(const State& xi, int n_cal, const Input& u,
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const std::vector<Measurement>& y, int n_meas,
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double t, double dt)
|
||||
: xi(xi), n_cal(n_cal), u(u), y(y), n_meas(n_meas), t(t), dt(dt) {}
|
||||
|
||||
//========================================================================
|
||||
// Symmetry Group Implementation - Group Elements and actions
|
||||
//========================================================================
|
||||
/**
|
||||
*
|
||||
* @param A Attitude element of Rot3 type
|
||||
* @param a Matrix3 bias element
|
||||
* @param B Rot3 vector containing calibration elements
|
||||
* Ouptuts a G object using Rot3 for rotation representation
|
||||
*/
|
||||
G::G(const Rot3& A, const Matrix3& a, const std::vector<Rot3>& B)
|
||||
: A(A), a(a), B(B) {}
|
||||
|
||||
/**
|
||||
* Defines the group operation (multiplication)
|
||||
* @param other Another Group element
|
||||
* @return G a product of two group elements
|
||||
* Uses Rot3 Hat, Rot3 Vee for multiplication
|
||||
*
|
||||
*/
|
||||
G G::operator*(const G& other) const {
|
||||
if (B.size() != other.B.size()) {
|
||||
throw std::invalid_argument("Group elements must have the same number of calibration elements");
|
||||
}
|
||||
|
||||
std::vector<Rot3> new_B;
|
||||
for (size_t i = 0; i < B.size(); i++) {
|
||||
new_B.push_back(B[i] * other.B[i]);
|
||||
}
|
||||
|
||||
return G(A * other.A,
|
||||
a + Rot3::Hat(A.matrix() * Rot3::Vee(other.a)),
|
||||
new_B);
|
||||
}
|
||||
|
||||
/**
|
||||
* Used to compute the Group inverse
|
||||
* @return The inverse of group element
|
||||
* Uses Rot3 inverse, Rot3 matrix, hat and vee functions
|
||||
* The left invariant property of the semi-direct product group structure is implemented here by using the -ve sign
|
||||
*/
|
||||
G G::inv() const {
|
||||
Matrix3 A_inv = A.inverse().matrix();
|
||||
|
||||
std::vector<Rot3> B_inv;
|
||||
for (const auto& b : B) {
|
||||
B_inv.push_back(b.inverse());
|
||||
}
|
||||
|
||||
return G(A.inverse(),
|
||||
-Rot3::Hat(A_inv * Rot3::Vee(a)),
|
||||
B_inv);
|
||||
}
|
||||
|
||||
/**
|
||||
* Creates the identity element of the group
|
||||
* @param n Number of calibration elements
|
||||
* @return the identity element
|
||||
* Uses Rot3 Identity and Matrix zero
|
||||
*/
|
||||
G G::identity(int n) {
|
||||
std::vector<Rot3> B(n, Rot3::Identity());
|
||||
return G(Rot3::Identity(), Matrix3::Zero(), B);
|
||||
}
|
||||
/**
|
||||
* Maps the tangent space elements to the group
|
||||
* @param x Vector in lie algebra
|
||||
* @return the group element G
|
||||
* Uses Rot3 expmap and Expmapderivative function
|
||||
*/
|
||||
G G::exp(const Vector& x) {
|
||||
if (x.size() < 6 || x.size() % 3 != 0) {
|
||||
throw std::invalid_argument("Wrong size, a vector with size multiple of 3 and at least 6 must be provided");
|
||||
}
|
||||
|
||||
int n = (x.size() - 6) / 3;
|
||||
Rot3 A = Rot3::Expmap(x.head<3>());
|
||||
|
||||
Vector3 a_vee = Rot3::ExpmapDerivative(-x.head<3>()) * x.segment<3>(3);
|
||||
Matrix3 a = Rot3::Hat(a_vee);
|
||||
|
||||
std::vector<Rot3> B;
|
||||
for (int i = 0; i < n; i++) {
|
||||
B.push_back(Rot3::Expmap(x.segment<3>(6 + 3*i)));
|
||||
}
|
||||
|
||||
return G(A, a, B);
|
||||
}
|
||||
|
||||
//========================================================================
|
||||
// Helper Functions Implementation
|
||||
//========================================================================
|
||||
|
||||
/**
|
||||
* Maps system dynamics to the symmetry group
|
||||
* @param xi State
|
||||
* @param u Input
|
||||
* @return Lifted input in Lie Algebra
|
||||
* Uses Vector zero & Rot3 inverse, matrix functions
|
||||
*/
|
||||
Vector lift(const State& xi, const Input& u) {
|
||||
int n = xi.S.size();
|
||||
Vector L = Vector::Zero(6 + 3 * n);
|
||||
|
||||
// First 3 elements
|
||||
L.head<3>() = u.w - xi.b;
|
||||
|
||||
// Next 3 elements
|
||||
L.segment<3>(3) = -u.W() * xi.b;
|
||||
|
||||
// Remaining elements
|
||||
for (int i = 0; i < n; i++) {
|
||||
L.segment<3>(6 + 3*i) = xi.S[i].inverse().matrix() * L.head<3>();
|
||||
}
|
||||
|
||||
return L;
|
||||
}
|
||||
/**
|
||||
* Implements group actions on the states
|
||||
* @param X A symmetry group element G consisting of the attitude, bias and the
|
||||
* calibration components X.a -> Rotation matrix containing the attitude X.b ->
|
||||
* A skew-symmetric matrix representing bias X.B -> A vector of Rotation
|
||||
* matrices for the calibration components
|
||||
* @param xi State object
|
||||
* xi.R -> Attitude (Rot3)
|
||||
* xi.b -> Gyroscope Bias(Vector 3)
|
||||
* xi.S -> Vector of calibration matrices(Rot3)
|
||||
* @return Transformed state
|
||||
* Uses the Rot3 inverse and Vee functions
|
||||
*/
|
||||
State stateAction(const G& X, const State& xi) {
|
||||
if (xi.S.size() != X.B.size()) {
|
||||
throw std::invalid_argument("Number of calibration states and B elements must match");
|
||||
}
|
||||
|
||||
std::vector<Rot3> new_S;
|
||||
for (size_t i = 0; i < X.B.size(); i++) {
|
||||
new_S.push_back(X.A.inverse() * xi.S[i] * X.B[i]);
|
||||
}
|
||||
|
||||
return State(xi.R * X.A,
|
||||
X.A.inverse().matrix() * (xi.b - Rot3::Vee(X.a)),
|
||||
new_S);
|
||||
}
|
||||
/**
|
||||
* Transforms the angular velocity measurements b/w frames
|
||||
* @param X A symmetry group element X with the components
|
||||
* @param u Inputs
|
||||
* @return Transformed inputs
|
||||
* Uses Rot3 Inverse, matrix and Vee functions and is critical for maintaining
|
||||
* the input equivariance
|
||||
*/
|
||||
Input velocityAction(const G& X, const Input& u) {
|
||||
return Input{X.A.inverse().matrix() * (u.w - Rot3::Vee(X.a)), u.Sigma};
|
||||
}
|
||||
/**
|
||||
* Transforms the Direction measurements based on the calibration type ( Eqn 6)
|
||||
* @param X Group element X
|
||||
* @param y Direction measurement y
|
||||
* @param idx Calibration index
|
||||
* @return Transformed direction
|
||||
* Uses Rot3 inverse, matric and Unit3 unitvector functions
|
||||
*/
|
||||
Vector3 outputAction(const G& X, const Direction& y, int idx) {
|
||||
if (idx == -1) {
|
||||
return X.A.inverse().matrix() * y.d.unitVector();
|
||||
} else {
|
||||
if (idx >= static_cast<int>(X.B.size())) {
|
||||
throw std::out_of_range("Calibration index out of range");
|
||||
}
|
||||
return X.B[idx].inverse().matrix() * y.d.unitVector();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Maps the error states to vector representations through exponential
|
||||
* coordinates
|
||||
* @param e error state
|
||||
* @return Vector with local coordinates
|
||||
* Uses Rot3 logamo for mapping rotations to the tangent space
|
||||
*/
|
||||
Vector local_coords(const State& e) {
|
||||
if (COORDINATE == "EXPONENTIAL") {
|
||||
Vector eps(6 + 3 * e.S.size());
|
||||
|
||||
// First 3 elements
|
||||
eps.head<3>() = Rot3::Logmap(e.R);
|
||||
|
||||
// Next 3 elements
|
||||
eps.segment<3>(3) = e.b;
|
||||
|
||||
// Remaining elements
|
||||
for (size_t i = 0; i < e.S.size(); i++) {
|
||||
eps.segment<3>(6 + 3*i) = Rot3::Logmap(e.S[i]);
|
||||
}
|
||||
|
||||
return eps;
|
||||
} else if (COORDINATE == "NORMAL") {
|
||||
throw std::runtime_error("Normal coordinate representation is not implemented yet");
|
||||
} else {
|
||||
throw std::invalid_argument("Invalid coordinate representation");
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Used to convert the vectorized errors back to state space
|
||||
* @param eps Local coordinates in the exponential parameterization
|
||||
* @return State object corresponding to these coordinates
|
||||
* Uses Rot3 expmap through the G::exp() function
|
||||
*/
|
||||
State local_coords_inv(const Vector& eps) {
|
||||
G X = G::exp(eps);
|
||||
|
||||
if (COORDINATE == "EXPONENTIAL") {
|
||||
std::vector<Rot3> S = X.B;
|
||||
return State(X.A, eps.segment<3>(3), S);
|
||||
} else if (COORDINATE == "NORMAL") {
|
||||
throw std::runtime_error("Normal coordinate representation is not implemented yet");
|
||||
} else {
|
||||
throw std::invalid_argument("Invalid coordinate representation");
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Computes the differential of a state action at the identity of the symmetry
|
||||
* group
|
||||
* @param xi State object Xi representing the point at which to evaluate the
|
||||
* differential
|
||||
* @return A matrix representing the jacobian of the state action
|
||||
* Uses numericalDifferential, and Rot3 expmap, logmap
|
||||
*/
|
||||
Matrix stateActionDiff(const State& xi) {
|
||||
std::function<Vector(const Vector&)> coordsAction =
|
||||
[&xi](const Vector& U) {
|
||||
return local_coords(stateAction(G::exp(U), xi));
|
||||
};
|
||||
|
||||
Vector zeros = Vector::Zero(6 + 3 * xi.S.size());
|
||||
Matrix differential = numericalDifferential(coordsAction, zeros);
|
||||
return differential;
|
||||
}
|
||||
|
||||
//========================================================================
|
||||
// Equivariant Filter (EqF) Implementation
|
||||
//========================================================================
|
||||
/**
|
||||
* Initializes the EqF with state dimension validation and computes lifted
|
||||
* innovation mapping
|
||||
* @param Sigma Initial covariance
|
||||
* @param n Number of calibration states
|
||||
* @param m Number of sensors
|
||||
* Uses SelfAdjointSolver, completeOrthoganalDecomposition().pseudoInverse()
|
||||
*/
|
||||
EqF::EqF(const Matrix& Sigma, int n, int m)
|
||||
: dof(6 + 3 * n), n_cal(n), X_hat(G::identity(n)),
|
||||
Sigma(Sigma), xi_0(State::identity(n)) {
|
||||
|
||||
if (Sigma.rows() != dof || Sigma.cols() != dof) {
|
||||
throw std::invalid_argument("Initial covariance dimensions must match the degrees of freedom");
|
||||
}
|
||||
|
||||
// Check positive semi-definite
|
||||
Eigen::SelfAdjointEigenSolver<Matrix> eigensolver(Sigma);
|
||||
if (eigensolver.eigenvalues().minCoeff() < -1e-10) {
|
||||
throw std::invalid_argument("Covariance matrix must be semi-positive definite");
|
||||
}
|
||||
|
||||
if (n < 0) {
|
||||
throw std::invalid_argument("Number of calibration states must be non-negative");
|
||||
}
|
||||
|
||||
if (m <= 1) {
|
||||
throw std::invalid_argument("Number of direction sensors must be at least 2");
|
||||
}
|
||||
|
||||
// Compute differential of phi
|
||||
Dphi0 = stateActionDiff(xi_0);
|
||||
InnovationLift = Dphi0.completeOrthogonalDecomposition().pseudoInverse();
|
||||
}
|
||||
/**
|
||||
* Computes the internal group state to a physical state estimate
|
||||
* @return Current state estimate
|
||||
*/
|
||||
State EqF::stateEstimate() const {
|
||||
return stateAction(X_hat, xi_0);
|
||||
}
|
||||
/**
|
||||
* Implements the prediction step of the EqF using system dynamics and
|
||||
* covariance propagation and advances the filter state by symmtery-preserving
|
||||
* dynamics.Uses a Lie group integrator scheme for discrete time propagation
|
||||
* @param u Angular velocity measurements
|
||||
* @param dt time steps
|
||||
* Updated internal state and covariance
|
||||
*/
|
||||
void EqF::propagation(const Input& u, double dt) {
|
||||
State state_est = stateEstimate();
|
||||
Vector L = lift(state_est, u);
|
||||
|
||||
Matrix Phi_DT = stateTransitionMatrix(u, dt);
|
||||
Matrix Bt = inputMatrixBt();
|
||||
|
||||
Matrix tempSigma = blockDiag(u.Sigma,
|
||||
repBlock(1e-9 * Matrix3::Identity(), n_cal));
|
||||
Matrix M_DT = (Bt * tempSigma * Bt.transpose()) * dt;
|
||||
|
||||
X_hat = X_hat * G::exp(L * dt);
|
||||
Sigma = Phi_DT * Sigma * Phi_DT.transpose() + M_DT;
|
||||
}
|
||||
/**
|
||||
* Implements the correction step of the filter using discrete measurements
|
||||
* Computes the measurement residual, Kalman gain and the updates both the state
|
||||
* and covariance
|
||||
*
|
||||
* @param y Measurements
|
||||
*/
|
||||
void EqF::update(const Measurement& y) {
|
||||
if (y.cal_idx > n_cal) {
|
||||
throw std::invalid_argument("Calibration index out of range");
|
||||
}
|
||||
|
||||
// Get vector representations for checking
|
||||
Vector3 y_vec = y.y.d.unitVector();
|
||||
Vector3 d_vec = y.d.d.unitVector();
|
||||
|
||||
// Skip update if any NaN values are present
|
||||
if (std::isnan(y_vec[0]) || std::isnan(y_vec[1]) || std::isnan(y_vec[2]) ||
|
||||
std::isnan(d_vec[0]) || std::isnan(d_vec[1]) || std::isnan(d_vec[2])) {
|
||||
return; // Skip this measurement
|
||||
}
|
||||
|
||||
Matrix Ct = measurementMatrixC(y.d, y.cal_idx);
|
||||
Vector3 action_result = outputAction(X_hat.inv(), y.y, y.cal_idx);
|
||||
Vector3 delta_vec = Rot3::Hat(y.d.d.unitVector()) * action_result;
|
||||
Matrix Dt = outputMatrixDt(y.cal_idx);
|
||||
Matrix S = Ct * Sigma * Ct.transpose() + Dt * y.Sigma * Dt.transpose();
|
||||
Matrix K = Sigma * Ct.transpose() * S.inverse();
|
||||
Vector Delta = InnovationLift * K * delta_vec;
|
||||
X_hat = G::exp(Delta) * X_hat;
|
||||
Sigma = (Matrix::Identity(dof, dof) - K * Ct) * Sigma;
|
||||
}
|
||||
/**
|
||||
* Computes linearized continuous time state matrix
|
||||
* @param u Angular velocity
|
||||
* @return Linearized state matrix
|
||||
* Uses Matrix zero and Identity functions
|
||||
*/
|
||||
Matrix EqF::stateMatrixA(const Input& u) const {
|
||||
Matrix3 W0 = velocityAction(X_hat.inv(), u).W();
|
||||
Matrix A1 = Matrix::Zero(6, 6);
|
||||
|
||||
if (COORDINATE == "EXPONENTIAL") {
|
||||
A1.block<3, 3>(0, 3) = -Matrix3::Identity();
|
||||
A1.block<3, 3>(3, 3) = W0;
|
||||
Matrix A2 = repBlock(W0, n_cal);
|
||||
return blockDiag(A1, A2);
|
||||
} else if (COORDINATE == "NORMAL") {
|
||||
throw std::runtime_error("Normal coordinate representation is not implemented yet");
|
||||
} else {
|
||||
throw std::invalid_argument("Invalid coordinate representation");
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Computes the discrete time state transition matrix
|
||||
* @param u Angular velocity
|
||||
* @param dt time step
|
||||
* @return State transition matrix in discrete time
|
||||
*/
|
||||
Matrix EqF::stateTransitionMatrix(const Input& u, double dt) const {
|
||||
Matrix3 W0 = velocityAction(X_hat.inv(), u).W();
|
||||
Matrix Phi1 = Matrix::Zero(6, 6);
|
||||
|
||||
Matrix3 Phi12 = -dt * (Matrix3::Identity() + (dt / 2) * W0 + ((dt*dt) / 6) * W0 * W0);
|
||||
Matrix3 Phi22 = Matrix3::Identity() + dt * W0 + ((dt*dt) / 2) * W0 * W0;
|
||||
|
||||
if (COORDINATE == "EXPONENTIAL") {
|
||||
Phi1.block<3, 3>(0, 0) = Matrix3::Identity();
|
||||
Phi1.block<3, 3>(0, 3) = Phi12;
|
||||
Phi1.block<3, 3>(3, 3) = Phi22;
|
||||
Matrix Phi2 = repBlock(Phi22, n_cal);
|
||||
return blockDiag(Phi1, Phi2);
|
||||
} else if (COORDINATE == "NORMAL") {
|
||||
throw std::runtime_error("Normal coordinate representation is not implemented yet");
|
||||
} else {
|
||||
throw std::invalid_argument("Invalid coordinate representation");
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Computes the input uncertainty propagation matrix
|
||||
* @return
|
||||
* Uses the blockdiag matrix
|
||||
*/
|
||||
Matrix EqF::inputMatrixBt() const {
|
||||
if (COORDINATE == "EXPONENTIAL") {
|
||||
Matrix B1 = blockDiag(X_hat.A.matrix(), X_hat.A.matrix());
|
||||
Matrix B2;
|
||||
|
||||
for (const auto& B : X_hat.B) {
|
||||
if (B2.size() == 0) {
|
||||
B2 = B.matrix();
|
||||
} else {
|
||||
B2 = blockDiag(B2, B.matrix());
|
||||
}
|
||||
}
|
||||
|
||||
return blockDiag(B1, B2);
|
||||
} else if (COORDINATE == "NORMAL") {
|
||||
throw std::runtime_error("Normal coordinate representation is not implemented yet");
|
||||
} else {
|
||||
throw std::invalid_argument("Invalid coordinate representation");
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Computes the linearized measurement matrix. The structure depends on whether
|
||||
* the sensor has a calibration state
|
||||
* @param d reference direction
|
||||
* @param idx Calibration index
|
||||
* @return Measurement matrix
|
||||
* Uses the matrix zero, Rot3 hat and the Unitvector functions
|
||||
*/
|
||||
Matrix EqF::measurementMatrixC(const Direction& d, int idx) const {
|
||||
Matrix Cc = Matrix::Zero(3, 3 * n_cal);
|
||||
|
||||
// If the measurement is related to a sensor that has a calibration state
|
||||
if (idx >= 0) {
|
||||
// Set the correct 3x3 block in Cc
|
||||
Cc.block<3, 3>(0, 3 * idx) = Rot3::Hat(d.d.unitVector());
|
||||
}
|
||||
|
||||
Matrix3 wedge_d = Rot3::Hat(d.d.unitVector());
|
||||
|
||||
// Create the combined matrix
|
||||
Matrix temp(3, 6 + 3 * n_cal);
|
||||
temp.block<3, 3>(0, 0) = wedge_d;
|
||||
temp.block<3, 3>(0, 3) = Matrix3::Zero();
|
||||
temp.block(0, 6, 3, 3 * n_cal) = Cc;
|
||||
|
||||
return wedge_d * temp;
|
||||
}
|
||||
/**
|
||||
* Computes the measurement uncertainty propagation matrix
|
||||
* @param idx Calibration index
|
||||
* @return Returns B[idx] for calibrated sensors, A for uncalibrated
|
||||
*/
|
||||
Matrix EqF::outputMatrixDt(int idx) const {
|
||||
// If the measurement is related to a sensor that has a calibration state
|
||||
if (idx >= 0) {
|
||||
if (idx >= static_cast<int>(X_hat.B.size())) {
|
||||
throw std::out_of_range("Calibration index out of range");
|
||||
}
|
||||
return X_hat.B[idx].matrix();
|
||||
} else {
|
||||
return X_hat.A.matrix();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
} // namespace abc_eqf_lib
|
||||
|
||||
|
|
|
|||
|
|
@ -15,6 +15,346 @@
|
|||
using namespace abc_eqf_lib;
|
||||
using namespace gtsam;
|
||||
|
||||
//========================================================================
|
||||
// Data Processing Functions
|
||||
//========================================================================
|
||||
|
||||
/**
|
||||
* Load data from CSV file into a vector of Data objects for the EqF
|
||||
*
|
||||
* CSV format:
|
||||
* - t: Time
|
||||
* - q_w, q_x, q_y, q_z: True attitude quaternion
|
||||
* - b_x, b_y, b_z: True bias
|
||||
* - cq_w_0, cq_x_0, cq_y_0, cq_z_0: True calibration quaternion
|
||||
* - w_x, w_y, w_z: Angular velocity measurements
|
||||
* - std_w_x, std_w_y, std_w_z: Angular velocity measurement standard deviations
|
||||
* - std_b_x, std_b_y, std_b_z: Bias process noise standard deviations
|
||||
* - y_x_0, y_y_0, y_z_0, y_x_1, y_y_1, y_z_1: Direction measurements
|
||||
* - std_y_x_0, std_y_y_0, std_y_z_0, std_y_x_1, std_y_y_1, std_y_z_1: Direction measurement standard deviations
|
||||
* - d_x_0, d_y_0, d_z_0, d_x_1, d_y_1, d_z_1: Reference directions
|
||||
*
|
||||
* @param filename Path to the CSV file
|
||||
* @param startRow First row to load (default: 0)
|
||||
* @param maxRows Maximum number of rows to load (default: all)
|
||||
* @param downsample Downsample factor (default: 1, which means no downsampling)
|
||||
* @return Vector of Data objects
|
||||
*/
|
||||
std::vector<Data> loadDataFromCSV(const std::string& filename,
|
||||
int startRow = 0,
|
||||
int maxRows = -1,
|
||||
int downsample = 1);
|
||||
|
||||
/**
|
||||
* Process data with EqF and print summary results
|
||||
* @param filter Initialized EqF filter
|
||||
* @param data_list Vector of Data objects to process
|
||||
* @param printInterval Progress indicator interval (used internally)
|
||||
*/
|
||||
void processDataWithEqF(EqF& filter, const std::vector<Data>& data_list, int printInterval = 10);
|
||||
|
||||
//========================================================================
|
||||
// Data Processing Functions Implementation
|
||||
//========================================================================
|
||||
|
||||
/**
|
||||
* @brief Loads the test data from the csv file
|
||||
* @param filename path to the csv file is specified
|
||||
* @param startRow First row to load based on csv, 0 by default
|
||||
* @param maxRows maximum rows to load, defaults to all rows
|
||||
* @param downsample Downsample factor, default 1
|
||||
* @return A list of data objects
|
||||
*/
|
||||
|
||||
|
||||
|
||||
std::vector<Data> loadDataFromCSV(const std::string& filename,
|
||||
int startRow,
|
||||
int maxRows,
|
||||
int downsample) {
|
||||
std::vector<Data> data_list;
|
||||
std::ifstream file(filename);
|
||||
|
||||
if (!file.is_open()) {
|
||||
throw std::runtime_error("Failed to open file: " + filename);
|
||||
}
|
||||
|
||||
std::cout << "Loading data from " << filename << "..." << std::flush;
|
||||
|
||||
std::string line;
|
||||
int lineNumber = 0;
|
||||
int rowCount = 0;
|
||||
int errorCount = 0;
|
||||
double prevTime = 0.0;
|
||||
|
||||
// Skip header
|
||||
std::getline(file, line);
|
||||
lineNumber++;
|
||||
|
||||
// Skip to startRow
|
||||
while (lineNumber < startRow && std::getline(file, line)) {
|
||||
lineNumber++;
|
||||
}
|
||||
|
||||
// Read data
|
||||
while (std::getline(file, line) && (maxRows == -1 || rowCount < maxRows)) {
|
||||
lineNumber++;
|
||||
|
||||
// Apply downsampling
|
||||
if ((lineNumber - startRow - 1) % downsample != 0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
std::istringstream ss(line);
|
||||
std::string token;
|
||||
std::vector<double> values;
|
||||
|
||||
// Parse line into values
|
||||
while (std::getline(ss, token, ',')) {
|
||||
try {
|
||||
values.push_back(std::stod(token));
|
||||
} catch (const std::exception& e) {
|
||||
errorCount++;
|
||||
values.push_back(0.0); // Use default value
|
||||
}
|
||||
}
|
||||
|
||||
// Check if we have enough values
|
||||
if (values.size() < 39) {
|
||||
errorCount++;
|
||||
continue;
|
||||
}
|
||||
|
||||
// Extract values
|
||||
double t = values[0];
|
||||
double dt = (rowCount == 0) ? 0.0 : t - prevTime;
|
||||
prevTime = t;
|
||||
|
||||
// Create ground truth state
|
||||
Quaternion quat(values[1], values[2], values[3], values[4]); // w, x, y, z
|
||||
Rot3 R = Rot3(quat);
|
||||
|
||||
Vector3 b(values[5], values[6], values[7]);
|
||||
|
||||
Quaternion calQuat(values[8], values[9], values[10], values[11]); // w, x, y, z
|
||||
std::vector<Rot3> S = {Rot3(calQuat)};
|
||||
|
||||
State xi(R, b, S);
|
||||
|
||||
// Create input
|
||||
Vector3 w(values[12], values[13], values[14]);
|
||||
|
||||
// Create input covariance matrix (6x6)
|
||||
// First 3x3 block for angular velocity, second 3x3 block for bias process noise
|
||||
Matrix inputCov = Matrix::Zero(6, 6);
|
||||
inputCov(0, 0) = values[15] * values[15]; // std_w_x^2
|
||||
inputCov(1, 1) = values[16] * values[16]; // std_w_y^2
|
||||
inputCov(2, 2) = values[17] * values[17]; // std_w_z^2
|
||||
inputCov(3, 3) = values[18] * values[18]; // std_b_x^2
|
||||
inputCov(4, 4) = values[19] * values[19]; // std_b_y^2
|
||||
inputCov(5, 5) = values[20] * values[20]; // std_b_z^2
|
||||
|
||||
Input u{w, inputCov};
|
||||
|
||||
// Create measurements
|
||||
std::vector<Measurement> measurements;
|
||||
|
||||
// First measurement (calibrated sensor, cal_idx = 0)
|
||||
Vector3 y0(values[21], values[22], values[23]);
|
||||
Vector3 d0(values[33], values[34], values[35]);
|
||||
|
||||
// Normalize vectors if needed
|
||||
if (abs(y0.norm() - 1.0) > 1e-5) y0.normalize();
|
||||
if (abs(d0.norm() - 1.0) > 1e-5) d0.normalize();
|
||||
|
||||
// Measurement covariance
|
||||
Matrix3 covY0 = Matrix3::Zero();
|
||||
covY0(0, 0) = values[27] * values[27]; // std_y_x_0^2
|
||||
covY0(1, 1) = values[28] * values[28]; // std_y_y_0^2
|
||||
covY0(2, 2) = values[29] * values[29]; // std_y_z_0^2
|
||||
|
||||
// Create measurement
|
||||
measurements.push_back(Measurement(y0, d0, covY0, 0));
|
||||
|
||||
// Second measurement (calibrated sensor, cal_idx = -1)
|
||||
Vector3 y1(values[24], values[25], values[26]);
|
||||
Vector3 d1(values[36], values[37], values[38]);
|
||||
|
||||
// Normalize vectors if needed
|
||||
if (abs(y1.norm() - 1.0) > 1e-5) y1.normalize();
|
||||
if (abs(d1.norm() - 1.0) > 1e-5) d1.normalize();
|
||||
|
||||
// Measurement covariance
|
||||
Matrix3 covY1 = Matrix3::Zero();
|
||||
covY1(0, 0) = values[30] * values[30]; // std_y_x_1^2
|
||||
covY1(1, 1) = values[31] * values[31]; // std_y_y_1^2
|
||||
covY1(2, 2) = values[32] * values[32]; // std_y_z_1^2
|
||||
|
||||
// Create measurement
|
||||
measurements.push_back(Measurement(y1, d1, covY1, -1));
|
||||
|
||||
// Create Data object and add to list
|
||||
data_list.push_back(Data(xi, 1, u, measurements, 2, t, dt));
|
||||
|
||||
rowCount++;
|
||||
|
||||
// Show loading progress every 1000 rows
|
||||
if (rowCount % 1000 == 0) {
|
||||
std::cout << "." << std::flush;
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << " Done!" << std::endl;
|
||||
std::cout << "Loaded " << data_list.size() << " data points";
|
||||
|
||||
if (errorCount > 0) {
|
||||
std::cout << " (" << errorCount << " errors encountered)";
|
||||
}
|
||||
|
||||
std::cout << std::endl;
|
||||
|
||||
return data_list;
|
||||
}
|
||||
/**
|
||||
* @brief Takes in the data and runs an EqF on it and reports the results
|
||||
* @param filter Initialized EqF filter
|
||||
* @param data_list std::vector<Data>
|
||||
* @param printInterval Progress indicator
|
||||
* Prints the performance statstics like average error etc
|
||||
* Uses Rot3 between, logmap and rpy functions
|
||||
*/
|
||||
void processDataWithEqF(EqF& filter, const std::vector<Data>& data_list, int printInterval) {
|
||||
if (data_list.empty()) {
|
||||
std::cerr << "No data to process" << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
std::cout << "Processing " << data_list.size() << " data points with EqF..." << std::endl;
|
||||
|
||||
// Track performance metrics
|
||||
std::vector<double> att_errors;
|
||||
std::vector<double> bias_errors;
|
||||
std::vector<double> cal_errors;
|
||||
|
||||
// Track time for performance measurement
|
||||
auto start = std::chrono::high_resolution_clock::now();
|
||||
|
||||
int totalMeasurements = 0;
|
||||
int validMeasurements = 0;
|
||||
|
||||
// Define constant for converting radians to degrees
|
||||
const double RAD_TO_DEG = 180.0 / M_PI;
|
||||
|
||||
// Print a progress indicator
|
||||
int progressStep = data_list.size() / 10; // 10 progress updates
|
||||
if (progressStep < 1) progressStep = 1;
|
||||
|
||||
std::cout << "Progress: ";
|
||||
|
||||
for (size_t i = 0; i < data_list.size(); i++) {
|
||||
const Data& data = data_list[i];
|
||||
|
||||
// Propagate filter with current input and time step
|
||||
filter.propagation(data.u, data.dt);
|
||||
|
||||
// Process all measurements
|
||||
for (const auto& y : data.y) {
|
||||
totalMeasurements++;
|
||||
|
||||
// Skip invalid measurements
|
||||
if (y.y.hasNaN() || y.d.hasNaN()) {
|
||||
continue;
|
||||
}
|
||||
|
||||
try {
|
||||
filter.update(y);
|
||||
validMeasurements++;
|
||||
} catch (const std::exception& e) {
|
||||
std::cerr << "Error updating at t=" << data.t
|
||||
<< ": " << e.what() << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
// Get current state estimate
|
||||
State estimate = filter.stateEstimate();
|
||||
|
||||
// Calculate errors
|
||||
Vector3 att_error = Rot3::Logmap(data.xi.R.between(estimate.R));
|
||||
Vector3 bias_error = estimate.b - data.xi.b;
|
||||
Vector3 cal_error = Vector3::Zero();
|
||||
if (!data.xi.S.empty() && !estimate.S.empty()) {
|
||||
cal_error = Rot3::Logmap(data.xi.S[0].between(estimate.S[0]));
|
||||
}
|
||||
|
||||
// Store errors
|
||||
att_errors.push_back(att_error.norm());
|
||||
bias_errors.push_back(bias_error.norm());
|
||||
cal_errors.push_back(cal_error.norm());
|
||||
|
||||
// Show progress dots
|
||||
if (i % progressStep == 0) {
|
||||
std::cout << "." << std::flush;
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << " Done!" << std::endl;
|
||||
|
||||
auto end = std::chrono::high_resolution_clock::now();
|
||||
std::chrono::duration<double> elapsed = end - start;
|
||||
|
||||
// Calculate average errors
|
||||
double avg_att_error = 0.0;
|
||||
double avg_bias_error = 0.0;
|
||||
double avg_cal_error = 0.0;
|
||||
|
||||
if (!att_errors.empty()) {
|
||||
avg_att_error = std::accumulate(att_errors.begin(), att_errors.end(), 0.0) / att_errors.size();
|
||||
avg_bias_error = std::accumulate(bias_errors.begin(), bias_errors.end(), 0.0) / bias_errors.size();
|
||||
avg_cal_error = std::accumulate(cal_errors.begin(), cal_errors.end(), 0.0) / cal_errors.size();
|
||||
}
|
||||
|
||||
// Calculate final errors from last data point
|
||||
const Data& final_data = data_list.back();
|
||||
State final_estimate = filter.stateEstimate();
|
||||
Vector3 final_att_error = Rot3::Logmap(final_data.xi.R.between(final_estimate.R));
|
||||
Vector3 final_bias_error = final_estimate.b - final_data.xi.b;
|
||||
Vector3 final_cal_error = Vector3::Zero();
|
||||
if (!final_data.xi.S.empty() && !final_estimate.S.empty()) {
|
||||
final_cal_error = Rot3::Logmap(final_data.xi.S[0].between(final_estimate.S[0]));
|
||||
}
|
||||
|
||||
// Print summary statistics
|
||||
std::cout << "\n=== Filter Performance Summary ===" << std::endl;
|
||||
std::cout << "Processing time: " << elapsed.count() << " seconds" << std::endl;
|
||||
std::cout << "Processed measurements: " << totalMeasurements << " (valid: " << validMeasurements << ")" << std::endl;
|
||||
|
||||
// Average errors
|
||||
std::cout << "\n-- Average Errors --" << std::endl;
|
||||
std::cout << "Attitude: " << (avg_att_error * RAD_TO_DEG) << "°" << std::endl;
|
||||
std::cout << "Bias: " << avg_bias_error << std::endl;
|
||||
std::cout << "Calibration: " << (avg_cal_error * RAD_TO_DEG) << "°" << std::endl;
|
||||
|
||||
// Final errors
|
||||
std::cout << "\n-- Final Errors --" << std::endl;
|
||||
std::cout << "Attitude: " << (final_att_error.norm() * RAD_TO_DEG) << "°" << std::endl;
|
||||
std::cout << "Bias: " << final_bias_error.norm() << std::endl;
|
||||
std::cout << "Calibration: " << (final_cal_error.norm() * RAD_TO_DEG) << "°" << std::endl;
|
||||
|
||||
// Print a brief comparison of final estimate vs ground truth
|
||||
std::cout << "\n-- Final State vs Ground Truth --" << std::endl;
|
||||
std::cout << "Attitude (RPY) - Estimate: "
|
||||
<< (final_estimate.R.rpy() * RAD_TO_DEG).transpose() << "° | Truth: "
|
||||
<< (final_data.xi.R.rpy() * RAD_TO_DEG).transpose() << "°" << std::endl;
|
||||
std::cout << "Bias - Estimate: " << final_estimate.b.transpose()
|
||||
<< " | Truth: " << final_data.xi.b.transpose() << std::endl;
|
||||
|
||||
if (!final_estimate.S.empty() && !final_data.xi.S.empty()) {
|
||||
std::cout << "Calibration (RPY) - Estimate: "
|
||||
<< (final_estimate.S[0].rpy() * RAD_TO_DEG).transpose() << "° | Truth: "
|
||||
<< (final_data.xi.S[0].rpy() * RAD_TO_DEG).transpose() << "°" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Main function for the EqF demo
|
||||
* @param argc Number of arguments
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
cmake_minimum_required(VERSION 3.5...3.22)
|
||||
cmake_minimum_required(VERSION 3.5)
|
||||
project(METIS)
|
||||
|
||||
# Add flags for currect directory and below
|
||||
|
|
@ -65,4 +65,4 @@ if (TARGET metis AND GKlib_COPTIONS)
|
|||
separate_arguments(GKlib_COPTIONS)
|
||||
# Declare those flags as to-be-imported in "client libraries", i.e. "gtsam"
|
||||
target_compile_options(metis INTERFACE ${GKlib_COPTIONS})
|
||||
endif()
|
||||
endif()
|
||||
|
|
@ -86,10 +86,7 @@ endforeach(subdir)
|
|||
|
||||
# To add additional sources to gtsam when building the full library (static or shared)
|
||||
# append the subfolder with _srcs appended to the end to this list
|
||||
set(gtsam_srcs ${3rdparty_srcs}
|
||||
../examples/ABC_EQF_Demo.cpp
|
||||
../examples/ABC_EQF.cpp
|
||||
../examples/ABC_EQF.h)
|
||||
set(gtsam_srcs ${3rdparty_srcs})
|
||||
foreach(subdir ${gtsam_subdirs})
|
||||
list(APPEND gtsam_srcs ${${subdir}_srcs})
|
||||
endforeach(subdir)
|
||||
|
|
|
|||
Loading…
Reference in New Issue