int32 UFaceFXMatineeControl::AddKeyframe( float Time, UInterpTrackInst* TrackInst, EInterpCurveMode InitInterpMode )
{
UFaceFXMatineeControlInst* TrackInstFaceFX = CastChecked<UFaceFXMatineeControlInst>(TrackInst);
FFaceFXTrackKey NewKey;
NewKey.Time = Time;
//insert and order by time
int32 i = 0;
for(i = 0; i < Keys.Num() && Keys[i].Time < Time; ++i);
Keys.Insert(NewKey, i);
UpdateKeyframe(i, TrackInst);
return i;
}
void
EstimatorFull::UpdateCamera(const Eigen::Vector3d &p_c_v, const Eigen::Quaterniond &q_c_v,
const Eigen::Matrix<double,6,6> &R,
bool absolute, bool isKeyframe, double dLambda )
{
Eigen::QuaternionAd q_c_kf;
Eigen::Vector3d p_c_kf;
if (absolute)
{
q_c_kf = Eigen::QuaternionAd(q_c_v.conjugate() * q_kf_v.toQuat().conjugate());
p_c_kf = q_kf_v.toQuat().toRotationMatrix() * (p_c_v - p_kf_v);
}
else
{
q_c_kf.q = q_c_v;
p_c_kf = p_c_v;
}
Matrix3d C_q_w_v = q_w_v.toQuat().toRotationMatrix();
Matrix3d C_q_i_w = q_i_w.toQuat().toRotationMatrix();
Matrix3d C_q_c_i = q_c_i.toQuat().toRotationMatrix();
// Build Jacobian
Matrix<double,3,28> H_p;
H_p <<
C_q_w_v.transpose() * exp(lambda),
Matrix3d::Zero(),
-C_q_w_v.transpose()*C_q_i_w.transpose()*crossMat(p_c_i)*exp(lambda),
Matrix3d::Zero(),
Matrix3d::Zero(),
(C_q_w_v.transpose()*(C_q_i_w.transpose()*p_c_i + p_i_w) + p_w_v) * exp(lambda),
C_q_w_v.transpose()*C_q_i_w.transpose()*exp(lambda),
Matrix3d::Zero(),
Matrix3d::Identity()*exp(lambda),
-C_q_w_v.transpose()*crossMat( p_i_w + C_q_i_w.transpose()*p_c_i )*exp(lambda);
Matrix<double,3,28> H_q;
H_q <<
Matrix3d::Zero(),
Matrix3d::Zero(),
C_q_c_i,
Matrix3d::Zero(),
Matrix3d::Zero(),
Vector3d::Zero(),
Matrix3d::Zero(),
Matrix3d::Identity(),
Matrix3d::Zero(),
C_q_c_i*C_q_i_w;
Matrix<double,6,28> H;
H << H_p, H_q;
// Calculate residual
Vector3d r_p = p_c_kf - ( C_q_w_v.transpose()*(p_i_w + C_q_i_w.transpose()*p_c_i) + p_w_v ) * exp(lambda);
Quaterniond r_q = (q_c_kf.toQuat()*( q_c_i.toQuat()*q_i_w.toQuat()*q_w_v.toQuat() ).conjugate()).conjugate();
Matrix<double,6,1> r;
r << r_p, 2*r_q.x(), 2*r_q.y(), 2*r_q.z();
// Calculate kalmn gain
Matrix<double,6,6> S = H*P*H.transpose() + R;
//K = P*H.transpose()*S^-1;
//TODO: use cholsky to solve K*S = P*H.transposed()?
// (K*S)' = (P*H')'
// S'*K' = H*P'
// K' = S.transpose.ldlt().solve(H*P.transpose)
Matrix<double,28,6> K = (S.ldlt().solve(H*P)).transpose(); // P and S are symmetric
//Matrix<double,28,6> K = P*H.transpose()*S.inverse();//S.lu().solve(H*P).transpose();
Matrix<double,28,1> x_error = K*r;
// Apply Kalman gain
ApplyCorrection( x_error );
Matrix<double,28,28> T = Matrix<double,28,28>::Identity() - K*H;
P = T*P*T.transpose() + K*R*K.transpose();
// Symmetrize
P = (P + P.transpose())/2.0;
if (isKeyframe)
{
// Add new keyframe from current position/camera pose
UpdateKeyframe( );
// Add noise to lambda
P(15,15) += dLambda;
// Update keyframe reference, if given path is absolute
if (absolute)
{
p_kf_v = p_c_v;
q_kf_v.q = q_c_v;
}
}
}