97 lines
2.7 KiB
C++
97 lines
2.7 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file LIEKF_NavstateExample.cpp
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* @brief LIEKF on NavState (SE_2(3)) with IMU (predict) and GPS (update)
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* @date April 25, 2025
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* @authors Scott Baker, Matt Kielo, Frank Dellaert
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*/
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/OptionalJacobian.h>
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#include <gtsam/navigation/LIEKF.h>
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#include <gtsam/navigation/NavState.h>
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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/**
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* @brief Left-invariant dynamics for NavState.
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* @param imu 6×1 vector [a; ω]: linear acceleration and angular velocity.
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* @return 9×1 tangent: [ω; 0₃; a].
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*/
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Vector9 dynamics(const Vector6& imu) {
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auto a = imu.head<3>();
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auto w = imu.tail<3>();
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Vector9 xi;
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xi << w, Vector3::Zero(), a;
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return xi;
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}
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/**
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* @brief GPS measurement model: returns position and its Jacobian.
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* @param X Current state.
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* @param H Optional 3×9 Jacobian w.r.t. X.
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* @return 3×1 position vector.
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*/
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Vector3 h_gps(const NavState& X, OptionalJacobian<3, 9> H = {}) {
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if (H) *H << Z_3x3, Z_3x3, X.R().matrix();
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return X.t();
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}
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int main() {
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// Initial state & covariances
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NavState X0; // R=I, v=0, t=0
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Matrix9 P0 = Matrix9::Identity() * 0.1;
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LIEKF<NavState> ekf(X0, P0);
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// Noise & timestep
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double dt = 1.0;
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Matrix9 Q = Matrix9::Identity() * 0.01;
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Matrix3 R = Matrix3::Identity() * 0.5;
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// Two IMU samples [a; ω]
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Vector6 imu1;
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imu1 << 0.1, 0, 0, 0, 0.2, 0;
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Vector6 imu2;
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imu2 << 0, 0.3, 0, 0.4, 0, 0;
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// Two GPS fixes
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Vector3 z1;
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z1 << 0.3, 0, 0;
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Vector3 z2;
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z2 << 0.6, 0, 0;
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cout << "=== Init ===\nX: " << ekf.state() << "\nP: " << ekf.covariance()
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<< "\n\n";
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// --- first predict/update ---
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ekf.predict(dynamics(imu1), dt, Q);
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cout << "--- After predict 1 ---\nX: " << ekf.state()
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<< "\nP: " << ekf.covariance() << "\n\n";
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ekf.update(h_gps, z1, R);
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cout << "--- After update 1 ---\nX: " << ekf.state()
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<< "\nP: " << ekf.covariance() << "\n\n";
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// --- second predict/update ---
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ekf.predict(dynamics(imu2), dt, Q);
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cout << "--- After predict 2 ---\nX: " << ekf.state()
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<< "\nP: " << ekf.covariance() << "\n\n";
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ekf.update(h_gps, z2, R);
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cout << "--- After update 2 ---\nX: " << ekf.state()
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<< "\nP: " << ekf.covariance() << "\n";
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return 0;
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}
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