gtsam/gtsam_unstable/dynamics/SimpleHelicopter.h

200 lines
7.3 KiB
C++

/*
* @file SimpleHelicopter.h
* @brief Implement SimpleHelicopter discrete dynamics model and variational integrator,
* following [Kobilarov09siggraph]
* @author Duy-Nguyen Ta
*/
#pragma once
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/base/numericalDerivative.h>
#include <cmath>
namespace gtsam {
/**
* Implement the Reconstruction equation: \f$ g_{k+1} = g_k \exp (h\xi_k) \f$, where
* \f$ h \f$: timestep (parameter)
* \f$ g_{k+1}, g_{k} \f$: poses at the current and the next timestep
* \f$ \xi_k \f$: the body-fixed velocity (Lie algebra)
* It is somewhat similar to BetweenFactor, but treats the body-fixed velocity
* \f$ \xi_k \f$ as a variable. So it is a three-way factor.
* Note: this factor is necessary if one needs to smooth the entire graph. It's not needed
* in sequential update method.
*/
class Reconstruction : public NoiseModelFactor3<Pose3, Pose3, Vector6> {
double h_; // time step
typedef NoiseModelFactor3<Pose3, Pose3, Vector6> Base;
public:
Reconstruction(Key gKey1, Key gKey, Key xiKey, double h, double mu = 1000.0) :
Base(noiseModel::Constrained::All(6, std::abs(mu)), gKey1, gKey,
xiKey), h_(h) {
}
virtual ~Reconstruction() {}
/// @return a deep copy of this factor
virtual gtsam::NonlinearFactor::shared_ptr clone() const {
return boost::static_pointer_cast<gtsam::NonlinearFactor>(
gtsam::NonlinearFactor::shared_ptr(new Reconstruction(*this))); }
/** \f$ log((g_k\exp(h\xi_k))^{-1}g_{k+1}) = 0, with optional derivatives */
Vector evaluateError(const Pose3& gk1, const Pose3& gk, const Vector6& xik,
boost::optional<Matrix&> H1 = boost::none,
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none) const {
Matrix6 D_exphxi_xi;
Pose3 exphxi = Pose3::Expmap(h_ * xik, H3 ? &D_exphxi_xi : 0);
Matrix6 D_gkxi_gk, D_gkxi_exphxi;
Pose3 gkxi = gk.compose(exphxi, D_gkxi_gk, H3 ? &D_gkxi_exphxi : 0);
Matrix6 D_hx_gk1, D_hx_gkxi;
Pose3 hx = gkxi.between(gk1, (H2 || H3) ? &D_hx_gkxi : 0, H1 ? &D_hx_gk1 : 0);
Matrix6 D_log_hx;
Vector error = Pose3::Logmap(hx, D_log_hx);
if (H1) *H1 = D_log_hx * D_hx_gk1;
if (H2 || H3) {
Matrix6 D_log_gkxi = D_log_hx * D_hx_gkxi;
if (H2) *H2 = D_log_gkxi * D_gkxi_gk;
if (H3) *H3 = D_log_gkxi * D_gkxi_exphxi * D_exphxi_xi * h_;
}
return error;
}
};
/**
* Implement the Discrete Euler-Poincare' equation:
*/
class DiscreteEulerPoincareHelicopter : public NoiseModelFactor3<Vector6, Vector6, Pose3> {
double h_; /// time step
Matrix Inertia_; /// Inertia tensors Inertia = [ J 0; 0 M ]
Vector Fu_; /// F is the 6xc Control matrix, where c is the number of control variables uk, which directly change the vehicle pose (e.g., gas/brake/speed)
/// F(.) is actually a function of the shape variables, which do not change the pose, but affect the vehicle's shape, e.g. steering wheel.
/// Fu_ encodes everything we need to know about the vehicle's dynamics.
double m_; /// mass. For gravity external force f_ext, which has a fixed formula in this case.
// TODO: Fk_ and f_ext should be generalized as functions (factor nodes) on control signals and poses/velocities.
// This might be needed in control or system identification problems.
// We treat them as constant here, since the control inputs are to specify.
typedef NoiseModelFactor3<Vector6, Vector6, Pose3> Base;
public:
DiscreteEulerPoincareHelicopter(Key xiKey1, Key xiKey_1, Key gKey,
double h, const Matrix& Inertia, const Vector& Fu, double m,
double mu = 1000.0) :
Base(noiseModel::Constrained::All(6, std::abs(mu)), xiKey1, xiKey_1, gKey),
h_(h), Inertia_(Inertia), Fu_(Fu), m_(m) {
}
virtual ~DiscreteEulerPoincareHelicopter() {}
/// @return a deep copy of this factor
virtual gtsam::NonlinearFactor::shared_ptr clone() const {
return boost::static_pointer_cast<gtsam::NonlinearFactor>(
gtsam::NonlinearFactor::shared_ptr(new DiscreteEulerPoincareHelicopter(*this))); }
/** DEP, with optional derivatives
* pk - pk_1 - h_*Fu_ - h_*f_ext = 0
* where pk = CT_TLN(h*xi_k)*Inertia*xi_k
* pk_1 = CT_TLN(-h*xi_k_1)*Inertia*xi_k_1
* */
Vector evaluateError(const Vector6& xik, const Vector6& xik_1, const Pose3& gk,
boost::optional<Matrix&> H1 = boost::none,
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none) const {
Vector muk = Inertia_*xik;
Vector muk_1 = Inertia_*xik_1;
// Apply the inverse right-trivialized tangent (derivative) map of the exponential map,
// using the trapezoidal Lie-Newmark (TLN) scheme, to a vector.
// TLN is just a first order approximation of the dExpInv_exp above, detailed in [Kobilarov09siggraph]
// C_TLN formula: I6 - 1/2 ad[xi].
Matrix D_adjThxik_muk, D_adjThxik1_muk1;
Vector pk = muk - 0.5*Pose3::adjointTranspose(h_*xik, muk, D_adjThxik_muk);
Vector pk_1 = muk_1 - 0.5*Pose3::adjointTranspose(-h_*xik_1, muk_1, D_adjThxik1_muk1);
Matrix D_gravityBody_gk;
Point3 gravityBody = gk.rotation().unrotate(Point3(0.0, 0.0, -9.81*m_), D_gravityBody_gk, boost::none);
Vector f_ext = (Vector(6) << 0.0, 0.0, 0.0, gravityBody.x(), gravityBody.y(), gravityBody.z()).finished();
Vector hx = pk - pk_1 - h_*Fu_ - h_*f_ext;
if (H1) {
Matrix D_pik_xi = Inertia_-0.5*(h_*D_adjThxik_muk + Pose3::adjointMap(h_*xik).transpose()*Inertia_);
*H1 = D_pik_xi;
}
if (H2) {
Matrix D_pik1_xik1 = Inertia_-0.5*(-h_*D_adjThxik1_muk1 + Pose3::adjointMap(-h_*xik_1).transpose()*Inertia_);
*H2 = -D_pik1_xik1;
}
if (H3) {
*H3 = Z_6x6;
insertSub(*H3, -h_*D_gravityBody_gk, 3, 0);
}
return hx;
}
#if 0
Vector computeError(const Vector6& xik, const Vector6& xik_1, const Pose3& gk) const {
Vector pk = Pose3::dExpInv_exp(h_*xik).transpose()*Inertia_*xik;
Vector pk_1 = Pose3::dExpInv_exp(-h_*xik_1).transpose()*Inertia_*xik_1;
Point3 gravityBody = gk.rotation().unrotate(Point3(0.0, 0.0, -9.81*m_));
Vector f_ext = (Vector(6) << 0.0, 0.0, 0.0, gravityBody.x(), gravityBody.y(), gravityBody.z());
Vector hx = pk - pk_1 - h_*Fu_ - h_*f_ext;
return hx;
}
Vector evaluateError(const Vector6& xik, const Vector6& xik_1, const Pose3& gk,
boost::optional<Matrix&> H1 = boost::none,
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none) const {
if (H1) {
(*H1) = numericalDerivative31(
boost::function<Vector(const Vector6&, const Vector6&, const Pose3&)>(
boost::bind(&DiscreteEulerPoincareHelicopter::computeError, *this, _1, _2, _3)
),
xik, xik_1, gk, 1e-5
);
}
if (H2) {
(*H2) = numericalDerivative32(
boost::function<Vector(const Vector6&, const Vector6&, const Pose3&)>(
boost::bind(&DiscreteEulerPoincareHelicopter::computeError, *this, _1, _2, _3)
),
xik, xik_1, gk, 1e-5
);
}
if (H3) {
(*H3) = numericalDerivative33(
boost::function<Vector(const Vector6&, const Vector6&, const Pose3&)>(
boost::bind(&DiscreteEulerPoincareHelicopter::computeError, *this, _1, _2, _3)
),
xik, xik_1, gk, 1e-5
);
}
return computeError(xik, xik_1, gk);
}
#endif
};
} /* namespace gtsam */