opengv::transformations_t
opengv::absolute_pose::gp3p(
const AbsoluteAdapterBase & adapter,
const std::vector<int> & indices )
{
assert(indices.size()>2);
Eigen::Matrix3d f;
Eigen::Matrix3d v;
Eigen::Matrix3d p;
for(size_t i = 0; i < 3; i++)
{
f.col(i) = adapter.getBearingVector(indices[i]);
rotation_t R = adapter.getCamRotation(indices[i]);
//unrotate the bearingVectors already so the camera rotation doesn't appear
//in the problem
f.col(i) = R * f.col(i);
v.col(i) = adapter.getCamOffset(indices[i]);
p.col(i) = adapter.getPoint(indices[i]);
}
transformations_t solutions;
modules::gp3p_main(f,v,p,solutions);
return solutions;
}
void mrpt::vision::pnp::rpnp::calcampose(Eigen::MatrixXd& XXc, Eigen::MatrixXd& XXw, Eigen::Matrix3d& R2, Eigen::Vector3d& t2)
{
Eigen::MatrixXd X = XXc;
Eigen::MatrixXd Y = XXw;
Eigen::MatrixXd K = Eigen::MatrixXd::Identity(n, n) - Eigen::MatrixXd::Ones(n, n) * 1 / n;
Eigen::VectorXd ux, uy;
uy = X.rowwise().mean();
ux = Y.rowwise().mean();
// Need to verify sigmax2
double sigmax2 = (((X*K).array() * (X*K).array()).colwise().sum()).mean();
Eigen::MatrixXd SXY = Y*K*(X.transpose()) / n;
Eigen::JacobiSVD<Eigen::MatrixXd> svd(SXY, Eigen::ComputeThinU | Eigen::ComputeThinV);
Eigen::Matrix3d S = Eigen::MatrixXd::Identity(3, 3);
if (SXY.determinant() <0)
S(2, 2) = -1;
R2 = svd.matrixV()*S*svd.matrixU().transpose();
double c2 = (svd.singularValues().asDiagonal()*S).trace() / sigmax2;
t2 = uy - c2*R2*ux;
Eigen::Vector3d x, y, z;
x = R2.col(0);
y = R2.col(1);
z = R2.col(2);
if ((x.cross(y) - z).norm()>0.02)
R2.col(2) = -R2.col(2);
}
void tangent_and_bitangent(const Eigen::Vector3d & n_,
Eigen::Vector3d & t_, Eigen::Vector3d & b_)
{
double rmin = 0.99;
double n0 = n_(0), n1 = n_(1), n2 = n_(2);
if (std::abs(n0) <= rmin) {
rmin = std::abs(n0);
t_(0) = 0.0;
t_(1) = - n2 / std::sqrt(1.0 - std::pow(n0, 2));
t_(2) = n1 / std::sqrt(1.0 - std::pow(n0, 2));
}
if (std::abs(n1) <= rmin) {
rmin = std::abs(n1);
t_(0) = n2 / std::sqrt(1.0 - std::pow(n1, 2));
t_(1) = 0.0;
t_(2) = - n0 / std::sqrt(1.0 - std::pow(n1, 2));
}
if (std::abs(n2) <= rmin) {
rmin = std::abs(n2);
t_(0) = n1 / std::sqrt(1.0 - std::pow(n2, 2));
t_(1) = -n0 / std::sqrt(1.0 - std::pow(n2, 2));
t_(2) = 0.0;
}
b_ = n_.cross(t_);
// Check that the calculated Frenet-Serret frame is left-handed (levogiro)
// by checking that the determinant of the matrix whose columns are the normal,
// tangent and bitangent vectors has determinant 1 (the system is orthonormal!)
Eigen::Matrix3d M;
M.col(0) = n_;
M.col(1) = t_;
M.col(2) = b_;
if (boost::math::sign(M.determinant()) != 1) {
PCMSOLVER_ERROR("Frenet-Serret local frame is not left-handed!", BOOST_CURRENT_FUNCTION);
}
}
/*
* Creates a rotation matrix which describes how a point in an auxiliary
* coordinate system, whose x axis is desbibed by vec_along_x_axis and has
* a point on its xz-plane vec_on_xz_plane, rotates into the real coordinate
* system.
*/
void Cornea::createRotationMatrix(const Eigen::Vector3d &vec_along_x_axis,
const Eigen::Vector3d &vec_on_xz_plane,
Eigen::Matrix3d &R) {
// normalise pw
Eigen::Vector3d vec_on_xz_plane_n = vec_on_xz_plane.normalized();
// define helper variables x, y and z
// x
Eigen::Vector3d xn = vec_along_x_axis.normalized();
// y
Eigen::Vector3d tmp = vec_on_xz_plane_n.cross(xn);
Eigen::Vector3d yn = tmp.normalized();
// z
tmp = xn.cross(yn);
Eigen::Vector3d zn = tmp.normalized();
// create the rotation matrix
R.col(0) << xn(0), xn(1), xn(2);
R.col(1) << yn(0), yn(1), yn(2);
R.col(2) << zn(0), zn(1), zn(2);
}
Eigen::Quaterniond mesh_core::PlaneProjection::getOrientation() const
{
Eigen::Matrix3d m;
m.col(0) = x_axis_;
m.col(1) = y_axis_;
m.col(2) = normal_;
return Eigen::Quaterniond(m);
}
Eigen::Matrix3d Reaching::reorderHandAxes(const Eigen::Matrix3d& Q)
{
Eigen::Matrix3d R = Eigen::MatrixXd::Zero(3, 3);
R.col(params_.axis_order_[0]) = Q.col(0); // grasp approach vector
R.col(params_.axis_order_[1]) = Q.col(1); // hand axis
R.col(params_.axis_order_[2]) = Q.col(2); // hand binormal
return R;
}
void Reconstruction3D::RQdecomposition(Eigen::MatrixXd A, Eigen::Matrix3d &R, Eigen::Matrix3d &Q)
{
float c, s;
Eigen::Matrix3d Qx, Qy, Qz;
Eigen::Matrix3d M;
M << A(0, 0), A(0, 1), A(0, 2),
A(1, 0), A(1, 1), A(1, 2),
A(2, 0), A(2, 1), A(2, 2);
R = M;
Reconstruction3D::solveCS(c, s, R(2, 1), R(2, 2));
Qx << 1, 0, 0,
0, c, -s,
0, s, c;
R *= Qx;
Reconstruction3D::solveCS(c, s, R(2, 0), -R(2, 2));
Qy << c, 0, s,
0, 1, 0,
-s, 0, c;
R *= Qy;
Reconstruction3D::solveCS(c, s, R(1, 0), R(1, 1));
Qz << c, -s, 0,
s, c, 0,
0, 0, 1;
R *= Qz;
if (std::abs(R(1, 0)) > 0.00005 || std::abs(R(2, 0)) > 0.00005 || std::abs(R(2, 1)) > 0.00005)
std::cerr << "PROBLEM WITH RQdecomposition" << std::endl;;
// if(R(1,0)!= 0) R(1,0) = 0;
// if(R(2,0)!= 0) R(2,0) = 0;
// if(R(2,1)!= 0) R(2,1) = 0;
Q = Qz.transpose() * Qy.transpose() * Qx.transpose();
if (R(0, 0) < 0)
{
R.col(0) *= -1;
Q.row(0) *= -1;
}
if (R(1, 1) < 0)
{
R.col(1) *= -1;
Q.row(1) *= -1;
}
if (R(2, 2) < 0)
{
R.col(2) *= -1;
Q.row(2) *= -1;
}
}
bool determinantCorrection(Eigen::Matrix3d &Rot)
{
double det1 = Rot.determinant();
if( det1 < -0.9999999 && det1 > -1.0000001 )
{
// When a rotation matrix is a reflection because its determinant is -1
Rot.col(2) = -1.0*Rot.col(2); // Corrects the third column sign
std::cout << "Rotation matrix has det = -1, correct procedure to achieve det = 1\n";
return true;
}
return false;
}
/* most relevant */
Eigen::Matrix3d MerryMotionplanner::compute_orientation(Eigen::Vector3f plane_normal, Eigen::Vector3f major_axis) {
Eigen::Vector3d xvec_des, yvec_des, zvec_des;
Eigen::Matrix3d Rmat;
for (int i = 0; i < 3; i++) {
zvec_des[i] = -plane_normal[i]; //want tool z pointing OPPOSITE surface normal
xvec_des[i] = major_axis[i];
}
yvec_des = zvec_des.cross(xvec_des); //construct consistent right-hand triad
Rmat.col(0)= xvec_des;
Rmat.col(1)= yvec_des;
Rmat.col(2)= zvec_des;
return Rmat;
}
void
NurbsTools::pca (const vector_vec3d &data, ON_3dVector &mean, Eigen::Matrix3d &eigenvectors,
Eigen::Vector3d &eigenvalues)
{
if (data.empty ())
{
printf ("[NurbsTools::pca] Error, data is empty\n");
abort ();
}
mean = computeMean (data);
unsigned s = data.size ();
ON_Matrix Q(3, s);
for (unsigned i = 0; i < s; i++) {
Q[0][i] = data[i].x - mean.x;
Q[1][i] = data[i].y - mean.y;
Q[2][i] = data[i].z - mean.z;
}
ON_Matrix Qt = Q;
Qt.Transpose();
ON_Matrix oC;
oC.Multiply(Q,Qt);
Eigen::Matrix3d C(3,3);
for (unsigned i = 0; i < 3; i++) {
for (unsigned j = 0; j < 3; j++) {
C(i,j) = oC[i][j];
}
}
Eigen::SelfAdjointEigenSolver < Eigen::Matrix3d > eigensolver (C);
if (eigensolver.info () != Eigen::Success)
{
printf ("[NurbsTools::pca] Can not find eigenvalues.\n");
abort ();
}
for (int i = 0; i < 3; ++i)
{
eigenvalues (i) = eigensolver.eigenvalues () (2 - i);
if (i == 2)
eigenvectors.col (2) = eigenvectors.col (0).cross (eigenvectors.col (1));
else
eigenvectors.col (i) = eigensolver.eigenvectors ().col (2 - i);
}
}
void CEViewOptionsWidget::updateMillerPlane()
{
// View into a Miller plane
Camera *camera = m_glWidget->camera();
Eigen::Transform3d modelView;
modelView.setIdentity();
// OK, so we want to rotate to look along the normal at the plane
// So we convert <h k l> into a Cartesian normal
Eigen::Matrix3d cellMatrix = m_ext->unconvertLength(m_ext->currentCellMatrix()).transpose();
// Get miller indices:
const Eigen::Vector3d millerIndices
(static_cast<double>(ui.spin_mi_h->value()),
static_cast<double>(ui.spin_mi_k->value()),
static_cast<double>(ui.spin_mi_l->value()));
// Check to see if we have 0,0,0
// in which case, we do nothing
if (millerIndices.squaredNorm() < 0.5)
return;
const Eigen::Vector3d normalVector ((cellMatrix * millerIndices).normalized());
Eigen::Matrix3d rotation;
rotation.row(2) = normalVector;
rotation.row(0) = rotation.row(2).unitOrthogonal();
rotation.row(1) = rotation.row(2).cross(rotation.row(0));
// Translate camera to the center of the cell
const Eigen::Vector3d cellDiagonal =
cellMatrix.col(0) * m_glWidget->aCells() +
cellMatrix.col(1) * m_glWidget->bCells() +
cellMatrix.col(2) * m_glWidget->cCells();
modelView.translate(-cellDiagonal*0.5);
// Prerotate the camera to look down the specified normal
modelView.prerotate(rotation);
// Pretranslate in the negative Z direction
modelView.pretranslate(Eigen::Vector3d(0.0, 0.0,
-1.5 * cellDiagonal.norm()));
camera->setModelview(modelView);
// Call for a redraw
m_glWidget->update();
}
void msEntity::computeEigenInertia(Eigen::Matrix3d& evect,Eigen::Vector3d& eval) const {
evect=Eigen::Matrix3d::Zero();
eval=Eigen::Vector3d::Zero();
if(Elements.size()==0) return;
Eigen::Matrix3d Inertia=Matrix3d::Zero();
double A,B,C,D,E,F;A=B=C=D=E=F=0; double x,y,z,m;
msChildren<msElement>::const_iterator it=Elements.begin();
for(;it!=Elements.end();++it)
{
m=(*it)->getMass(getUnits()->getMass());
x=(*it)->getPosition()[0];
y=(*it)->getPosition()[1];
z=(*it)->getPosition()[2];
A+=m*(y*y+z*z);B+=m*(x*x+z*z);C+=m*(y*y+x*x);
D-=m*(z*y);E-=m*(x*z);F-=m*(x*y);
}
Inertia<<A,F,E,F,B,D,E,D,C;
Eigen::EigenSolver<Eigen::Matrix3d> eigensolver(Inertia);
for( size_t i=0; i<3 ;i++ ){
eval[i]= eigensolver.eigenvalues()[i].real();
for( size_t j=0; j<3 ; j++ ) evect.col(i)[j] = eigensolver.eigenvectors().col(i)[j].real();
}
}
void update_arrows() {
geometry_msgs::Point origin, arrow_x_tip, arrow_y_tip, arrow_z_tip;
Eigen::Matrix3d R;
Eigen::Quaterniond quat;
quat.x() = g_stamped_pose.pose.orientation.x;
quat.y() = g_stamped_pose.pose.orientation.y;
quat.z() = g_stamped_pose.pose.orientation.z;
quat.w() = g_stamped_pose.pose.orientation.w;
R = quat.toRotationMatrix();
Eigen::Vector3d x_vec, y_vec, z_vec;
double veclen = 0.2; //make the arrows this long
x_vec = R.col(0) * veclen;
y_vec = R.col(1) * veclen;
z_vec = R.col(2) * veclen;
//update the arrow markers w/ new pose:
origin = g_stamped_pose.pose.position;
arrow_x_tip = origin;
arrow_x_tip.x += x_vec(0);
arrow_x_tip.y += x_vec(1);
arrow_x_tip.z += x_vec(2);
arrow_marker_x.points.clear();
arrow_marker_x.points.push_back(origin);
arrow_marker_x.points.push_back(arrow_x_tip);
arrow_marker_x.header = g_stamped_pose.header;
arrow_y_tip = origin;
arrow_y_tip.x += y_vec(0);
arrow_y_tip.y += y_vec(1);
arrow_y_tip.z += y_vec(2);
arrow_marker_y.points.clear();
arrow_marker_y.points.push_back(origin);
arrow_marker_y.points.push_back(arrow_y_tip);
arrow_marker_y.header = g_stamped_pose.header;
arrow_z_tip = origin;
arrow_z_tip.x += z_vec(0);
arrow_z_tip.y += z_vec(1);
arrow_z_tip.z += z_vec(2);
arrow_marker_z.points.clear();
arrow_marker_z.points.push_back(origin);
arrow_marker_z.points.push_back(arrow_z_tip);
arrow_marker_z.header = g_stamped_pose.header;
}
ON_NurbsSurface
FittingSurface::initNurbsPCA (int order, NurbsDataSurface *m_data, ON_3dVector z)
{
ON_3dVector mean;
Eigen::Matrix3d eigenvectors;
Eigen::Vector3d eigenvalues;
unsigned s = m_data->interior.size ();
NurbsTools::pca (m_data->interior, mean, eigenvectors, eigenvalues);
m_data->mean = mean;
//m_data->eigenvectors = (*eigenvectors);
bool flip (false);
Eigen::Vector3d ez(z[0],z[1],z[2]);
if (eigenvectors.col (2).dot (ez) < 0.0)
flip = true;
eigenvalues = eigenvalues / s; // seems that the eigenvalues are dependent on the number of points (???)
ON_3dVector sigma(sqrt(eigenvalues[0]), sqrt(eigenvalues[1]), sqrt(eigenvalues[2]));
ON_NurbsSurface nurbs (3, false, order, order, order, order);
nurbs.MakeClampedUniformKnotVector (0, 1.0);
nurbs.MakeClampedUniformKnotVector (1, 1.0);
// +- 2 sigma -> 95,45 % aller Messwerte
double dcu = (4.0 * sigma[0]) / (nurbs.Order (0) - 1);
double dcv = (4.0 * sigma[1]) / (nurbs.Order (1) - 1);
ON_3dVector cv_t, cv;
Eigen::Vector3d ecv_t, ecv;
Eigen::Vector3d emean(mean[0], mean[1], mean[2]);
for (int i = 0; i < nurbs.Order (0); i++)
{
for (int j = 0; j < nurbs.Order (1); j++)
{
cv[0] = -2.0 * sigma[0] + dcu * i;
cv[1] = -2.0 * sigma[1] + dcv * j;
cv[2] = 0.0;
ecv (0) = -2.0 * sigma[0] + dcu * i;
ecv (1) = -2.0 * sigma[1] + dcv * j;
ecv (2) = 0.0;
ecv_t = eigenvectors * ecv + emean;
cv_t[0] = ecv_t (0);
cv_t[1] = ecv_t (1);
cv_t[2] = ecv_t (2);
if (flip)
nurbs.SetCV (nurbs.Order (0) - 1 - i, j, ON_3dPoint (cv_t[0], cv_t[1], cv_t[2]));
else
nurbs.SetCV (i, j, ON_3dPoint (cv_t[0], cv_t[1], cv_t[2]));
}
}
return nurbs;
}
void MarkerArrayVisualizer::drawGaussianDistributions(const char* ns, const std::vector<Eigen::Vector3d>& mu, const std::vector<Eigen::Matrix3d>& sigma, double probability, const std::vector<double>& offset)
{
visualization_msgs::MarkerArray marker_array;
const int size = mu.size();
for (int i=0; i<size; i++)
{
visualization_msgs::Marker marker;
marker.header.frame_id = "/world";
marker.header.stamp = ros::Time::now();
marker.ns = ns;
marker.id = i;
marker.type = visualization_msgs::Marker::SPHERE;
marker.action = visualization_msgs::Marker::ADD;
// axis: eigenvectors
// radius: eigenvalues
Eigen::JacobiSVD<Eigen::MatrixXd> svd(sigma[i], Eigen::ComputeThinU | Eigen::ComputeThinV);
const Eigen::VectorXd& r = svd.singularValues();
Eigen::Matrix3d Q = svd.matrixU();
// to make determinant 1
if (Q.determinant() < 0)
Q.col(2) *= -1.;
const Eigen::Quaterniond q(Q);
marker.pose.position.x = mu[i](0);
marker.pose.position.y = mu[i](1);
marker.pose.position.z = mu[i](2);
marker.pose.orientation.x = q.x();
marker.pose.orientation.y = q.y();
marker.pose.orientation.z = q.z();
marker.pose.orientation.w = q.w();
const double probability_radius = gaussianDistributionRadius3D(probability);
marker.scale.x = 2. * (probability_radius * std::sqrt(r[0]) + offset[i]);
marker.scale.y = 2. * (probability_radius * std::sqrt(r[1]) + offset[i]);
marker.scale.z = 2. * (probability_radius * std::sqrt(r[2]) + offset[i]);
marker.color.r = 1.0;
marker.color.g = 0.0;
marker.color.b = 0.0;
marker.color.a = 0.25;
marker.lifetime = ros::Duration();
marker_array.markers.push_back(marker);
}
publish(marker_array);
}
void MarkerArrayVisualizer::drawEllipsoids(const char* ns, const std::vector<Eigen::Vector3d>& center, const std::vector<Eigen::Matrix3d>& A)
{
visualization_msgs::MarkerArray marker_array;
const int size = center.size();
for (int i=0; i<size; i++)
{
visualization_msgs::Marker marker;
marker.header.frame_id = "/world";
marker.header.stamp = ros::Time::now();
marker.ns = ns;
marker.id = i;
marker.type = visualization_msgs::Marker::SPHERE;
marker.action = visualization_msgs::Marker::ADD;
// axis: eigenvectors
// radius: eigenvalues
Eigen::JacobiSVD<Eigen::MatrixXd> svd(A[i], Eigen::ComputeThinU | Eigen::ComputeThinV);
const Eigen::VectorXd& r = svd.singularValues();
Eigen::Matrix3d Q = svd.matrixU();
// to make determinant 1
if (Q.determinant() < 0)
Q.col(2) *= -1.;
const Eigen::Quaterniond q(Q);
marker.pose.position.x = center[i](0);
marker.pose.position.y = center[i](1);
marker.pose.position.z = center[i](2);
marker.pose.orientation.x = q.x();
marker.pose.orientation.y = q.y();
marker.pose.orientation.z = q.z();
marker.pose.orientation.w = q.w();
marker.scale.x = 2. * r(0);
marker.scale.y = 2. * r(1);
marker.scale.z = 2. * r(2);
marker.color.r = 1.0;
marker.color.g = 0.0;
marker.color.b = 0.0;
marker.color.a = 0.2;
marker.lifetime = ros::Duration();
marker_array.markers.push_back(marker);
}
publish(marker_array);
}
std::vector<tf::Quaternion> Reaching::calculateHandOrientations(const GraspEigen& grasp)
{
// calculate first hand orientation
Eigen::Matrix3d R = Eigen::MatrixXd::Zero(3, 3);
R.col(0) = -1.0 * grasp.approach_;
R.col(1) = grasp.axis_;
R.col(2) << R.col(0).cross(R.col(1));
// rotate by 180deg around the grasp approach vector to get the "opposite" hand orientation
Eigen::Transform<double, 3, Eigen::Affine> T(Eigen::AngleAxis<double>(M_PI, grasp.approach_));
// calculate second hand orientation
Eigen::Matrix3d Q = Eigen::MatrixXd::Zero(3, 3);
Q.col(0) = T * grasp.approach_;
Q.col(1) = T * grasp.axis_;
Q.col(2) << Q.col(0).cross(Q.col(1));
// reorder rotation matrix columns according to axes ordering of the robot hand
Eigen::Matrix3d R1 = reorderHandAxes(R);
Eigen::Matrix3d R2 = reorderHandAxes(Q);
// convert Eigen rotation matrices to TF quaternions and normalize them
tf::Matrix3x3 TF1, TF2;
tf::matrixEigenToTF(R1, TF1);
tf::matrixEigenToTF(R2, TF2);
tf::Quaternion quat1, quat2;
TF1.getRotation(quat1);
TF2.getRotation(quat2);
quat1.normalize();
quat2.normalize();
std::vector<tf::Quaternion> quats;
quats.push_back(quat1);
quats.push_back(quat2);
// for debugging
//tf::TransformBroadcaster br;
//tf::Transform transform;
//transform.setOrigin( tf::Vector3(grasp.center_(0), grasp.center_(1), grasp.center_(2)) );
//transform.setRotation( quat1 );
//br.sendTransform(tf::StampedTransform(transform, ros::Time::now(), "base", "carrot1"));
//ros::Duration(1.0).sleep();
//transform.setRotation( quat2 );
//br.sendTransform(tf::StampedTransform(transform, ros::Time::now(), "base", "carrot2"));
//ros::Duration(1.0).sleep();
//std::cout << "transform:\n" << transform.getOrigin() << std::endl;
return quats;
}
/**
* \brief create a marker from a chart and stores it into markers array
*/
virtual void createNodeMarker(const Chart &c)
{
if (!markers){
ROS_WARN_THROTTLE(30,"[ExplorerBase::%s]\tNo marker array to update is provided, visualization is disabled.",__func__);
return;
}
visualization_msgs::Marker disc;
disc.header.frame_id = mark_frame;
disc.header.stamp = ros::Time();
disc.lifetime = ros::Duration(0.5);
disc.ns = "Atlas Nodes";
disc.id = c.getId();
disc.type = visualization_msgs::Marker::CYLINDER;
disc.action = visualization_msgs::Marker::ADD;
disc.scale.x = c.getRadius()*2;
disc.scale.y = c.getRadius()*2;
disc.scale.z = 0.001;
disc.color.a = 0.75;
disc.color.r = 0.3;
disc.color.b = 0.35;
disc.color.g = 0.5;
Eigen::Matrix3d rot;
rot.col(0) = c.getTanBasisOne();
rot.col(1) = c.getTanBasisTwo();
rot.col(2) = c.getNormal();
Eigen::Quaterniond q(rot);
q.normalize();
disc.pose.orientation.x = q.x();
disc.pose.orientation.y = q.y();
disc.pose.orientation.z = q.z();
disc.pose.orientation.w = q.w();
disc.pose.position.x = c.getCenter()[0];
disc.pose.position.y = c.getCenter()[1];
disc.pose.position.z = c.getCenter()[2];
std::lock_guard<std::mutex> guard(*mtx_ptr);
markers->markers.push_back(disc);
}
void
NurbsTools::pca (const vector_vec3d &data, Eigen::Vector3d &mean, Eigen::Matrix3d &eigenvectors,
Eigen::Vector3d &eigenvalues)
{
if (data.empty ())
{
printf ("[NurbsTools::pca] Error, data is empty\n");
abort ();
}
mean = computeMean (data);
unsigned s = unsigned (data.size ());
Eigen::MatrixXd Q (3, s);
for (unsigned i = 0; i < s; i++)
Q.col (i) << (data[i] - mean);
Eigen::Matrix3d C = Q * Q.transpose ();
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> eigensolver (C);
if (eigensolver.info () != Success)
{
printf ("[NurbsTools::pca] Can not find eigenvalues.\n");
abort ();
}
for (int i = 0; i < 3; ++i)
{
eigenvalues (i) = eigensolver.eigenvalues () (2 - i);
if (i == 2)
eigenvectors.col (2) = eigenvectors.col (0).cross (eigenvectors.col (1));
else
eigenvectors.col (i) = eigensolver.eigenvectors ().col (2 - i);
}
}
ON_NurbsSurface
FittingSurface::initNurbsPCA (int order, NurbsDataSurface *m_data, Eigen::Vector3d z)
{
Eigen::Vector3d mean;
Eigen::Matrix3d eigenvectors;
Eigen::Vector3d eigenvalues;
unsigned s = m_data->interior.size ();
NurbsTools::pca (m_data->interior, mean, eigenvectors, eigenvalues);
m_data->mean = mean;
m_data->eigenvectors = eigenvectors;
bool flip (false);
if (eigenvectors.col (2).dot (z) < 0.0)
flip = true;
eigenvalues = eigenvalues / s; // seems that the eigenvalues are dependent on the number of points (???)
Eigen::Vector3d sigma (sqrt (eigenvalues (0)), sqrt (eigenvalues (1)), sqrt (eigenvalues (2)));
ON_NurbsSurface nurbs (3, false, order, order, order, order);
nurbs.MakeClampedUniformKnotVector (0, 1.0);
nurbs.MakeClampedUniformKnotVector (1, 1.0);
// +- 2 sigma -> 95,45 % aller Messwerte
double dcu = (4.0 * sigma (0)) / (nurbs.Order (0) - 1);
double dcv = (4.0 * sigma (1)) / (nurbs.Order (1) - 1);
Eigen::Vector3d cv_t, cv;
for (int i = 0; i < nurbs.Order (0); i++)
{
for (int j = 0; j < nurbs.Order (1); j++)
{
cv (0) = -2.0 * sigma (0) + dcu * i;
cv (1) = -2.0 * sigma (1) + dcv * j;
cv (2) = 0.0;
cv_t = eigenvectors * cv + mean;
if (flip)
nurbs.SetCV (nurbs.Order (0) - 1 - i, j, ON_3dPoint (cv_t (0), cv_t (1), cv_t (2)));
else
nurbs.SetCV (i, j, ON_3dPoint (cv_t (0), cv_t (1), cv_t (2)));
}
}
return nurbs;
}
template<typename PointT> void
pcl::GeneralizedIterativeClosestPoint<PointSource, PointTarget>::computeCovariances(typename pcl::PointCloud<PointT>::ConstPtr cloud,
const typename pcl::search::KdTree<PointT>::Ptr kdtree,
std::vector<Eigen::Matrix3d>& cloud_covariances)
{
if (k_correspondences_ > int (cloud->size ()))
{
PCL_ERROR ("[pcl::GeneralizedIterativeClosestPoint::computeCovariances] Number or points in cloud (%lu) is less than k_correspondences_ (%lu)!\n", cloud->size (), k_correspondences_);
return;
}
Eigen::Vector3d mean;
std::vector<int> nn_indecies; nn_indecies.reserve (k_correspondences_);
std::vector<float> nn_dist_sq; nn_dist_sq.reserve (k_correspondences_);
// We should never get there but who knows
if(cloud_covariances.size () < cloud->size ())
cloud_covariances.resize (cloud->size ());
typename pcl::PointCloud<PointT>::const_iterator points_iterator = cloud->begin ();
std::vector<Eigen::Matrix3d>::iterator matrices_iterator = cloud_covariances.begin ();
for(;
points_iterator != cloud->end ();
++points_iterator, ++matrices_iterator)
{
const PointT &query_point = *points_iterator;
Eigen::Matrix3d &cov = *matrices_iterator;
// Zero out the cov and mean
cov.setZero ();
mean.setZero ();
// Search for the K nearest neighbours
kdtree->nearestKSearch(query_point, k_correspondences_, nn_indecies, nn_dist_sq);
// Find the covariance matrix
for(int j = 0; j < k_correspondences_; j++) {
const PointT &pt = (*cloud)[nn_indecies[j]];
mean[0] += pt.x;
mean[1] += pt.y;
mean[2] += pt.z;
cov(0,0) += pt.x*pt.x;
cov(1,0) += pt.y*pt.x;
cov(1,1) += pt.y*pt.y;
cov(2,0) += pt.z*pt.x;
cov(2,1) += pt.z*pt.y;
cov(2,2) += pt.z*pt.z;
}
mean /= static_cast<double> (k_correspondences_);
// Get the actual covariance
for (int k = 0; k < 3; k++)
for (int l = 0; l <= k; l++)
{
cov(k,l) /= static_cast<double> (k_correspondences_);
cov(k,l) -= mean[k]*mean[l];
cov(l,k) = cov(k,l);
}
// Compute the SVD (covariance matrix is symmetric so U = V')
Eigen::JacobiSVD<Eigen::Matrix3d> svd(cov, Eigen::ComputeFullU);
cov.setZero ();
Eigen::Matrix3d U = svd.matrixU ();
// Reconstitute the covariance matrix with modified singular values using the column // vectors in V.
for(int k = 0; k < 3; k++) {
Eigen::Vector3d col = U.col(k);
double v = 1.; // biggest 2 singular values replaced by 1
if(k == 2) // smallest singular value replaced by gicp_epsilon
v = gicp_epsilon_;
cov+= v * col * col.transpose();
}
}
}
void EKFOA::process(const double delta_t, cv::Mat & frame, Eigen::Vector3d & rW, Eigen::Vector4d & qWR, Eigen::Matrix3d & axes_orientation_and_confidence, std::vector<Point3d> (& XYZs)[3], Delaunay & triangulation, Point3d & closest_point){
double time_total;
std::vector<cv::Point2f> features_to_add;
std::vector<Features_extra> features_extra;
/*
* EKF prediction (state and measurement prediction)
*/
time_total = (double)cv::getTickCount();
double time_prediction = (double)cv::getTickCount();
filter.predict_state_and_covariance(delta_t);
filter.compute_features_h(cam, features_extra);
time_prediction = (double)cv::getTickCount() - time_prediction;
// std::cout << "predict = " << time_prediction/((double)cvGetTickFrequency()*1000.) << "ms" << std::endl;
/*
* Sense and map management (delete features from EKF)
*/
double time_tracker = (double)cv::getTickCount();
motion_tracker.process(frame, features_extra, features_to_add);
//TODO: Why is optical flow returning points outside the image???
time_tracker = (double)cv::getTickCount() - time_tracker;
time_total = time_total + time_tracker; //do not count the time spent by the tracker
//Delete no longer seen features from the state, covariance matrix and the features_extra:
double time_del = (double)cv::getTickCount();
filter.delete_features(features_extra);
time_del = (double)cv::getTickCount() - time_del;
// std::cout << "delete = " << time_del/((double)cvGetTickFrequency()*1000.) << "ms" << std::endl;
/*
* EKF Update step and map management (add new features to EKF)
*/
double time_update = (double)cv::getTickCount();
filter.update(cam, features_extra);
time_update = (double)cv::getTickCount() - time_update;
// std::cout << "update = " << time_update/((double)cvGetTickFrequency()*1000.) << "ms" << std::endl;
//Add new features
double time_add = (double)cv::getTickCount();
filter.add_features_inverse_depth(cam, features_to_add);
time_add = (double)cv::getTickCount() - time_add;
// std::cout << "add_fea = " << time_add/((double)cvGetTickFrequency()*1000.) << "ms" << std::endl;
/*
* Triangulation, surface and GUI data setting:
*/
double time_triangulation = (double)cv::getTickCount();
std::vector< std::pair<Point2d, size_t> > triangle_list;
std::list<Triangle> triangles_list_3d;
const Eigen::VectorXd & x_k_k = filter.x_k_k();
const Eigen::MatrixXd & p_k_k = filter.p_k_k();
//Set the position, so the GUI can draw it:
rW = x_k_k.segment<3>(0);//current position
//Set the axes orientation and confidence:
axes_orientation_and_confidence.setIdentity();//axes_orientation_and_confidence stores in each column one axis (X, Y, Z)
axes_orientation_and_confidence *= 5; //make the lines larger, so they are actually informative
//Apply rotation matrix:
Eigen::Matrix3d qWR_R;//Rotation matrix of current orientation quaternion
qWR = x_k_k.segment<4>(3);
MotionModel::quaternion_matrix(qWR, qWR_R);
axes_orientation_and_confidence.applyOnTheLeft(qWR_R); // == R * axes_orientation_and_confidence
for (int axis=0 ; axis<axes_orientation_and_confidence.cols() ; axis++){
//Set the length to be 3*sigma:
axes_orientation_and_confidence.col(axis) *= 3*std::sqrt(p_k_k(axis, axis)); //the first 3 positions of the cov matrix define the confidence for the position
//Translate origin:
axes_orientation_and_confidence.col(axis) += rW;
}
int num_features = (x_k_k.rows()-13)/6;
XYZs[0].resize(num_features);
XYZs[1].resize(num_features);
XYZs[2].resize(num_features);
//Compute the 3d positions and inverse depth variances of all the points in the state
int i=0; //Feature counter
for (int start_feature=13 ; start_feature<x_k_k.rows() ; start_feature+=6){
const int feature_inv_depth_index = start_feature + 5;
//As with any normal distribution, nearly all (99.73%) of the possible depths lie within three standard deviations of the mean!
const double sigma_3 = std::sqrt(p_k_k(feature_inv_depth_index, feature_inv_depth_index)); //sqrt(depth_variance)
const Eigen::VectorXd & yi = x_k_k.segment(start_feature, 6);
Eigen::VectorXd point_close(x_k_k.segment(start_feature, 6));
Eigen::VectorXd point_far(x_k_k.segment(start_feature, 6));
//Change the depth of the feature copy, so that it is possible to represent the range between -3*sigma and 3*sigma:
point_close(5) += sigma_3;
point_far(5) -= sigma_3;
Eigen::Vector3d XYZ_mu = (Feature::compute_cartesian(yi)); //mu (mean)
Eigen::Vector3d XYZ_close = (Feature::compute_cartesian(point_close)); //mean + 3*sigma. (since inverted signs are also inverted)
Eigen::Vector3d XYZ_far = (Feature::compute_cartesian(point_far)); //mean - 3*sigma
//The center of the model is ALWAYS the current position of the camera/robot, so have to 'cancel' the current orientation (R_inv) and translation (rWC = x_k_k.head(3)):
//Note: It is nicer to do this in the GUI class, as it is only a presention/perspective change. But due to the structure, it was easier to do it here.
XYZs[0][i] = Point3d(XYZ_mu(0), XYZ_mu(1), XYZ_mu(2)); //mu (mean)
XYZs[1][i] = Point3d(XYZ_close(0), XYZ_close(1), XYZ_close(2)); //mean + 3*sigma. (since inverted signs are also inverted)
XYZs[2][i] = Point3d(XYZ_far(0), XYZ_far(1), XYZ_far(2)); //mean - 3*sigma
//If the size that contains the 99.73% of the inverse depth distribution is smaller than the current inverse depth, add it to the surface:
const double size_sigma_3 = std::abs(1.0/(x_k_k(feature_inv_depth_index)-sigma_3) - 1.0/(x_k_k(feature_inv_depth_index)+sigma_3));
if (size_sigma_3 < 1/x_k_k(feature_inv_depth_index)){
triangle_list.push_back(std::make_pair(Point2d(features_extra[i].z(0), features_extra[i].z(1)), i));
}
if (x_k_k(feature_inv_depth_index) < 0 ){
std::cout << "feature behind the camera!!! : idx=" << i << ", value=" << x_k_k(feature_inv_depth_index) << std::endl;
}
i++;
}
triangulation.insert(triangle_list.begin(), triangle_list.end());
cv::Scalar delaunay_color = cv::Scalar(255, 0, 0); //blue
for(Delaunay::Finite_faces_iterator fit = triangulation.finite_faces_begin(); fit != triangulation.finite_faces_end(); ++fit) {
const Delaunay::Face_handle & face = fit;
//face->vertex(i)->info() = index of the point in the observation list.
line(frame, features_extra[face->vertex(0)->info()].z_cv, features_extra[face->vertex(1)->info()].z_cv, delaunay_color, 1);
line(frame, features_extra[face->vertex(1)->info()].z_cv, features_extra[face->vertex(2)->info()].z_cv, delaunay_color, 1);
line(frame, features_extra[face->vertex(2)->info()].z_cv, features_extra[face->vertex(0)->info()].z_cv, delaunay_color, 1);
//Add the face of the linked 3d points of this 2d triangle:
triangles_list_3d.push_back(Triangle(XYZs[1][face->vertex(0)->info()], XYZs[1][face->vertex(1)->info()], XYZs[1][face->vertex(2)->info()])); //XYZs[1] == close
}
// constructs AABB tree
Tree tree(triangles_list_3d.begin(), triangles_list_3d.end());
if (tree.size()>0){
// compute closest point and squared distance
Point3d point_query(rW[0], rW[1], rW[2]);
closest_point = tree.closest_point(point_query);
// FT sqd = tree.squared_distance(point_query);
Eigen::Vector3d last_displacement_vector = last_position - rW;
// double repealing_force = 0;
// if (std::sqrt(sqd) < 0.2){
// std::cout << "can crash! " << std::endl;
// repealing_force = 1/std::sqrt(sqd);
// }
// std::cout << "distance = [distance, " << std::sqrt(sqd) << "];" << std::endl;
// std::cout << "repealing_force = [repealing_force, " << repealing_force << "];" << std::endl;
}
//remember this position
last_position = rW;
// std::cout << "certaint= " << p_k_k.diagonal().sum() << std::endl;
time_triangulation = (double)cv::getTickCount() - time_triangulation;
// std::cout << "Triang = " << time_triangulation/((double)cvGetTickFrequency()*1000.) << "ms" << std::endl;
time_total = (double)cv::getTickCount() - time_total;
// std::cout << "EKF = " << time_total/((double)cvGetTickFrequency()*1000.) << "ms" << std::endl;
// std::cout << "tracker = " << time_tracker/((double)cvGetTickFrequency()*1000.) << "ms" << std::endl;
}
void apply_small_rotation(Eigen::Matrix3d &rotation, double h, int i, int j)
{
const Eigen::Vector3d col_l = rotation.col(j) - rotation.col(i)*h;
rotation.col(i) += rotation.col(j) * h;
rotation.col(j) = col_l;
}
void init_poses() {
ROS_INFO("getting transforms from camera to PSMs");
//tf::TransformListener tfListener;
tf::StampedTransform tfResult_one, tfResult_two;
g_tfListener_ptr = new tf::TransformListener;
bool tferr = true;
int ntries = 0;
ROS_INFO("waiting for tf between base and camera...");
while (tferr) {
if (ntries > 5) break; //give up and accept default after this many tries
tferr = false;
try {
//The direction of the transform returned will be from the target_frame to the source_frame.
//Which if applied to data, will transform data in the source_frame into the target_frame. See tf/CoordinateFrameConventions#Transform_Direction
g_tfListener_ptr->lookupTransform("left_camera_optical_frame", "one_psm_base_link", ros::Time(0), tfResult_one);
g_tfListener_ptr->lookupTransform("left_camera_optical_frame", "two_psm_base_link", ros::Time(0), tfResult_two);
} catch (tf::TransformException &exception) {
ROS_WARN("%s", exception.what());
tferr = true;
ros::Duration(0.5).sleep(); // sleep for half a second
ros::spinOnce();
ntries++;
}
}
//default transform: need to match this up to camera calibration!
if (tferr) {
g_affine_lcamera_to_psm_one.translation() << -0.155, -0.03265, 0.0;
Eigen::Vector3d nvec, tvec, bvec;
nvec << -1, 0, 0;
tvec << 0, 1, 0;
bvec << 0, 0, -1;
Eigen::Matrix3d R;
R.col(0) = nvec;
R.col(1) = tvec;
R.col(2) = bvec;
g_affine_lcamera_to_psm_one.linear() = R;
g_affine_lcamera_to_psm_two.linear() = R;
g_affine_lcamera_to_psm_two.translation() << 0.145, -0.03265, 0.0;
ROS_WARN("using default transform");
} else {
ROS_INFO("tf is good");
//g_affine_lcamera_to_psm_one is the position/orientation of psm1 base frame w/rt left camera link frame
// need to extend this to camera optical frame
g_affine_lcamera_to_psm_one = transformTFToEigen(tfResult_one);
g_affine_lcamera_to_psm_two = transformTFToEigen(tfResult_two);
}
ROS_INFO("transform from left camera to psm one:");
cout << g_affine_lcamera_to_psm_one.linear() << endl;
cout << g_affine_lcamera_to_psm_one.translation().transpose() << endl;
ROS_INFO("transform from left camera to psm two:");
cout << g_affine_lcamera_to_psm_two.linear() << endl;
cout << g_affine_lcamera_to_psm_two.translation().transpose() << endl;
//now get initial poses:
ROS_INFO("waiting for tf between base and grippers...");
while (tferr) {
if (ntries > 5) break; //give up and accept default after this many tries
tferr = false;
try {
//The direction of the transform returned will be from the target_frame to the source_frame.
//Which if applied to data, will transform data in the source_frame into the target_frame. See tf/CoordinateFrameConventions#Transform_Direction
g_tfListener_ptr->lookupTransform("left_camera_optical_frame", "one_tool_tip_link", ros::Time(0), tfResult_one);
g_tfListener_ptr->lookupTransform("left_camera_optical_frame", "two_tool_tip_link", ros::Time(0), tfResult_two);
} catch (tf::TransformException &exception) {
ROS_WARN("%s", exception.what());
tferr = true;
ros::Duration(0.5).sleep(); // sleep for half a second
ros::spinOnce();
ntries++;
}
}
//default start pose, if can't get tf:
if (tferr) {
g_psm1_start_pose.translation() << -0.02, 0, 0.04;
g_psm2_start_pose.translation() << 0.02, 0, 0.04;
Eigen::Vector3d nvec, tvec, bvec;
nvec << -1, 0, 0;
tvec << 0, 1, 0;
bvec << 0, 0, -1;
Eigen::Matrix3d R;
R.col(0) = nvec;
R.col(1) = tvec;
R.col(2) = bvec;
g_psm1_start_pose.linear() = R;
g_psm2_start_pose.linear() = R;
ROS_WARN("using default start poses");
} else {
ROS_INFO("tf is good");
//g_affine_lcamera_to_psm_one is the position/orientation of psm1 base frame w/rt left camera link frame
// need to extend this to camera optical frame
g_psm1_start_pose = transformTFToEigen(tfResult_one);
g_psm2_start_pose = transformTFToEigen(tfResult_two);
}
ROS_INFO("psm1 gripper start pose:");
cout << g_psm1_start_pose.linear() << endl;
cout << g_psm1_start_pose.translation().transpose() << endl;
ROS_INFO("psm2 gripper start pose:");
cout << g_psm2_start_pose.linear() << endl;
cout << g_psm2_start_pose.translation().transpose() << endl;
//just to be safe:
g_O_entry_point(0) = 0.0;
g_O_entry_point(1) = 0.0;
g_O_entry_point(2) = 0.12;
g_O_exit_point = g_O_entry_point;
g_O_exit_point(0) += 0.02;
}
ON_NurbsSurface
FittingSurface::initNurbsPCABoundingBox (int order, NurbsDataSurface *m_data, ON_3dVector z)
{
ON_3dVector mean;
Eigen::Matrix3d eigenvectors;
Eigen::Vector3d eigenvalues;
unsigned s = m_data->interior.size ();
m_data->interior_param.clear ();
NurbsTools::pca (m_data->interior, mean, eigenvectors, eigenvalues);
m_data->mean = mean;
//m_data->eigenvectors = (*eigenvectors);
bool flip (false);
Eigen::Vector3d ez(z[0],z[1],z[2]);
if (eigenvectors.col (2).dot (ez) < 0.0)
flip = true;
eigenvalues = eigenvalues / s; // seems that the eigenvalues are dependent on the number of points (???)
Eigen::Matrix3d eigenvectors_inv = eigenvectors.inverse ();
ON_3dVector v_max(0.0, 0.0, 0.0);
ON_3dVector v_min(DBL_MAX, DBL_MAX, DBL_MAX);
Eigen::Vector3d emean(mean[0], mean[1], mean[2]);
for (unsigned i = 0; i < s; i++)
{
Eigen::Vector3d eint(m_data->interior[i][0], m_data->interior[i][1], m_data->interior[i][2]);
Eigen::Vector3d ep = eigenvectors_inv * (eint - emean);
ON_3dPoint p(ep (0), ep (1), ep(2));
m_data->interior_param.push_back (ON_2dPoint(p[0], p[1]));
if (p[0] > v_max[0])
v_max[0] = p[0];
if (p[1] > v_max[1])
v_max[1] = p[1];
if (p[2] > v_max[2])
v_max[2] = p[2];
if (p[0] < v_min[0])
v_min[0] = p[0];
if (p[1] < v_min[1])
v_min[1] = p[1];
if (p[2] < v_min[2])
v_min[2] = p[2];
}
for (unsigned i = 0; i < s; i++)
{
ON_2dVector &p = m_data->interior_param[i];
if (v_max[0] > v_min[0] && v_max[0] > v_min[0])
{
p[0] = (p[0] - v_min[0]) / (v_max[0] - v_min[0]);
p[1] = (p[1] - v_min[1]) / (v_max[1] - v_min[1]);
}
else
{
throw std::runtime_error ("[NurbsTools::initNurbsPCABoundingBox] Error: v_max <= v_min");
}
}
ON_NurbsSurface nurbs (3, false, order, order, order, order);
nurbs.MakeClampedUniformKnotVector (0, 1.0);
nurbs.MakeClampedUniformKnotVector (1, 1.0);
double dcu = (v_max[0] - v_min[0]) / (nurbs.Order (0) - 1);
double dcv = (v_max[1] - v_min[1]) / (nurbs.Order (1) - 1);
ON_3dPoint cv_t, cv;
Eigen::Vector3d ecv_t2, ecv2;
Eigen::Vector3d emean2(mean[0],mean[1],mean[2]);
for (int i = 0; i < nurbs.Order (0); i++)
{
for (int j = 0; j < nurbs.Order (1); j++)
{
cv[0] = v_min[0] + dcu * i;
cv[1] = v_min[1] + dcv * j;
cv[2] = 0.0;
ecv2 (0) = cv[0];
ecv2 (1) = cv[1];
ecv2 (2) = cv[2];
ecv_t2 = eigenvectors * ecv2 + emean2;
if (flip)
nurbs.SetCV (nurbs.Order (0) - 1 - i, j, cv_t);
else
nurbs.SetCV (i, j, cv_t);
}
}
return nurbs;
}
int P3P::computePoses(const Eigen::Matrix3d & feature_vectors, const Eigen::Matrix3d & world_points,
Eigen::Matrix<Eigen::Matrix<double, 3, 4>, 4, 1> & solutions)
{
// Extraction of world points
Eigen::Vector3d P1 = world_points.col(0);
Eigen::Vector3d P2 = world_points.col(1);
Eigen::Vector3d P3 = world_points.col(2);
// Verification that world points are not colinear
Eigen::Vector3d temp1 = P2 - P1;
Eigen::Vector3d temp2 = P3 - P1;
if (temp1.cross(temp2).norm() == 0)
{
return -1;
}
// Extraction of feature vectors
Eigen::Vector3d f1 = feature_vectors.col(0);
Eigen::Vector3d f2 = feature_vectors.col(1);
Eigen::Vector3d f3 = feature_vectors.col(2);
// Creation of intermediate camera frame
Eigen::Vector3d e1 = f1;
Eigen::Vector3d e3 = f1.cross(f2);
e3 = e3 / e3.norm();
Eigen::Vector3d e2 = e3.cross(e1);
Eigen::Matrix3d T;
T.row(0) = e1.transpose();
T.row(1) = e2.transpose();
T.row(2) = e3.transpose();
f3 = T * f3;
// Reinforce that f3(2,0) > 0 for having theta in [0;pi]
if (f3(2, 0) > 0)
{
f1 = feature_vectors.col(1);
f2 = feature_vectors.col(0);
f3 = feature_vectors.col(2);
e1 = f1;
e3 = f1.cross(f2);
e3 = e3 / e3.norm();
e2 = e3.cross(e1);
T.row(0) = e1.transpose();
T.row(1) = e2.transpose();
T.row(2) = e3.transpose();
f3 = T * f3;
P1 = world_points.col(1);
P2 = world_points.col(0);
P3 = world_points.col(2);
}
// Creation of intermediate world frame
Eigen::Vector3d n1 = P2 - P1;
n1 = n1 / n1.norm();
Eigen::Vector3d n3 = n1.cross(P3 - P1);
n3 = n3 / n3.norm();
Eigen::Vector3d n2 = n3.cross(n1);
Eigen::Matrix3d N;
N.row(0) = n1.transpose();
N.row(1) = n2.transpose();
N.row(2) = n3.transpose();
// Extraction of known parameters
P3 = N * (P3 - P1);
double d_12 = (P2 - P1).norm();
double f_1 = f3(0, 0) / f3(2, 0);
double f_2 = f3(1, 0) / f3(2, 0);
double p_1 = P3(0, 0);
double p_2 = P3(1, 0);
double cos_beta = f1.dot(f2);
double b = 1 / (1 - pow(cos_beta, 2)) - 1;
if (cos_beta < 0)
{
b = -sqrt(b);
}
else
{
b = sqrt(b);
}
// Definition of temporary variables for avoiding multiple computation
double f_1_pw2 = pow(f_1, 2);
double f_2_pw2 = pow(f_2, 2);
double p_1_pw2 = pow(p_1, 2);
double p_1_pw3 = p_1_pw2 * p_1;
double p_1_pw4 = p_1_pw3 * p_1;
double p_2_pw2 = pow(p_2, 2);
double p_2_pw3 = p_2_pw2 * p_2;
double p_2_pw4 = p_2_pw3 * p_2;
double d_12_pw2 = pow(d_12, 2);
double b_pw2 = pow(b, 2);
// Computation of factors of 4th degree polynomial
Eigen::Matrix<double, 5, 1> factors;
factors(0) = -f_2_pw2 * p_2_pw4 - p_2_pw4 * f_1_pw2 - p_2_pw4;
factors(1) = 2 * p_2_pw3 * d_12 * b + 2 * f_2_pw2 * p_2_pw3 * d_12 * b - 2 * f_2 * p_2_pw3 * f_1 * d_12;
factors(2) = -f_2_pw2 * p_2_pw2 * p_1_pw2 - f_2_pw2 * p_2_pw2 * d_12_pw2 * b_pw2 - f_2_pw2 * p_2_pw2 * d_12_pw2
+ f_2_pw2 * p_2_pw4 + p_2_pw4 * f_1_pw2 + 2 * p_1 * p_2_pw2 * d_12 + 2 * f_1 * f_2 * p_1 * p_2_pw2 * d_12 * b
- p_2_pw2 * p_1_pw2 * f_1_pw2 + 2 * p_1 * p_2_pw2 * f_2_pw2 * d_12 - p_2_pw2 * d_12_pw2 * b_pw2
- 2 * p_1_pw2 * p_2_pw2;
factors(3) = 2 * p_1_pw2 * p_2 * d_12 * b + 2 * f_2 * p_2_pw3 * f_1 * d_12 - 2 * f_2_pw2 * p_2_pw3 * d_12 * b
- 2 * p_1 * p_2 * d_12_pw2 * b;
factors(4) = -2 * f_2 * p_2_pw2 * f_1 * p_1 * d_12 * b + f_2_pw2 * p_2_pw2 * d_12_pw2 + 2 * p_1_pw3 * d_12
- p_1_pw2 * d_12_pw2 + f_2_pw2 * p_2_pw2 * p_1_pw2 - p_1_pw4 - 2 * f_2_pw2 * p_2_pw2 * p_1 * d_12
+ p_2_pw2 * f_1_pw2 * p_1_pw2 + f_2_pw2 * p_2_pw2 * d_12_pw2 * b_pw2;
// Computation of roots
Eigen::Matrix<double, 4, 1> realRoots;
P3P::solveQuartic(factors, realRoots);
// Backsubstitution of each solution
for (int i = 0; i < 4; ++i)
{
double cot_alpha = (-f_1 * p_1 / f_2 - realRoots(i) * p_2 + d_12 * b)
/ (-f_1 * realRoots(i) * p_2 / f_2 + p_1 - d_12);
double cos_theta = realRoots(i);
double sin_theta = sqrt(1 - pow((double)realRoots(i), 2));
double sin_alpha = sqrt(1 / (pow(cot_alpha, 2) + 1));
double cos_alpha = sqrt(1 - pow(sin_alpha, 2));
if (cot_alpha < 0)
{
cos_alpha = -cos_alpha;
}
Eigen::Vector3d C;
C(0) = d_12 * cos_alpha * (sin_alpha * b + cos_alpha);
C(1) = cos_theta * d_12 * sin_alpha * (sin_alpha * b + cos_alpha);
C(2) = sin_theta * d_12 * sin_alpha * (sin_alpha * b + cos_alpha);
C = P1 + N.transpose() * C;
Eigen::Matrix3d R;
R(0, 0) = -cos_alpha;
R(0, 1) = -sin_alpha * cos_theta;
R(0, 2) = -sin_alpha * sin_theta;
R(1, 0) = sin_alpha;
R(1, 1) = -cos_alpha * cos_theta;
R(1, 2) = -cos_alpha * sin_theta;
R(2, 0) = 0;
R(2, 1) = -sin_theta;
R(2, 2) = cos_theta;
R = N.transpose() * R.transpose() * T;
Eigen::Matrix<double, 3, 4> solution;
solution.block<3, 3>(0, 0) = R;
solution.col(3) = C;
solutions(i) = solution;
}
return 0;
}
visualization_msgs::MarkerArray PCTrackContainer::toMarkerGMMs()
{
double margin = 0.1;
double stepsize = 0.01;
visualization_msgs::MarkerArray gmmMarkers;
int cnt = 0;
for(int i=0;i<numTracks();i++){
// if(tracks.at(i)->lastFrame().time == currentT){
PCObject object;
object = tracks.at(i)->lastFrame().object;
int id = tracks.at(i)->id;
// make a gmm eval points of the object
for(int j=0;j<object.gmm.size();j++){
cnt ++;
visualization_msgs::Marker gmmMarker;
gmmMarker.header.frame_id = "/origin";
gmmMarker.header.stamp = ros::Time();
gmmMarker.ns = "gmm";
gmmMarker.id = cnt;
Gaussian gmm = object.gmm.at(j);
gmmMarker.type = visualization_msgs::Marker::SPHERE;
gmmMarker.action = visualization_msgs::Marker::ADD;
gmmMarker.lifetime = ros::Duration(0);
gmmMarker.pose.position.x = gmm.mean[0];
gmmMarker.pose.position.y = gmm.mean[1];
gmmMarker.pose.position.z = gmm.mean[2];
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> eigensolver(gmm.covariance);
Eigen::Vector3d eigenvalues = eigensolver.eigenvalues();
Eigen::Matrix3d eigenvectors = eigensolver.eigenvectors();
Eigen::Matrix3d rotation = eigenvectors;
double r11 = rotation.col(0)[0];
double r21 = rotation.col(0)[1];
double r31 = rotation.col(0)[2];
double r12 = rotation.col(1)[0];
double r22 = rotation.col(1)[1];
double r32 = rotation.col(1)[2];
double r13 = rotation.col(2)[0];
double r23 = rotation.col(2)[1];
double r33 = rotation.col(2)[2];
double q0 = ( r11 + r22 + r33 + 1.0f) / 4.0f;
double q1 = ( r11 - r22 - r33 + 1.0f) / 4.0f;
double q2 = (-r11 + r22 - r33 + 1.0f) / 4.0f;
double q3 = (-r11 - r22 + r33 + 1.0f) / 4.0f;
if(q0 < 0.0f) q0 = 0.0f;
if(q1 < 0.0f) q1 = 0.0f;
if(q2 < 0.0f) q2 = 0.0f;
if(q3 < 0.0f) q3 = 0.0f;
q0 = sqrt(q0);
q1 = sqrt(q1);
q2 = sqrt(q2);
q3 = sqrt(q3);
if(q0 >= q1 && q0 >= q2 && q0 >= q3) {
q0 *= +1.0f;
q1 *= SIGN(r32 - r23);
q2 *= SIGN(r13 - r31);
q3 *= SIGN(r21 - r12);
} else if(q1 >= q0 && q1 >= q2 && q1 >= q3) {
q0 *= SIGN(r32 - r23);
q1 *= +1.0f;
q2 *= SIGN(r21 + r12);
q3 *= SIGN(r13 + r31);
} else if(q2 >= q0 && q2 >= q1 && q2 >= q3) {
q0 *= SIGN(r13 - r31);
q1 *= SIGN(r21 + r12);
q2 *= +1.0f;
q3 *= SIGN(r32 + r23);
} else if(q3 >= q0 && q3 >= q1 && q3 >= q2) {
q0 *= SIGN(r21 - r12);
q1 *= SIGN(r31 + r13);
q2 *= SIGN(r32 + r23);
q3 *= +1.0f;
} else {
printf("coding error\n");
}
float rq = NORM(q0, q1, q2, q3);
q0 /= rq;
q1 /= rq;
q2 /= rq;
q3 /= rq;
gmmMarker.pose.orientation.w = q0;
gmmMarker.pose.orientation.x = q1;
gmmMarker.pose.orientation.y = q2;
gmmMarker.pose.orientation.z = q3;
gmmMarker.frame_locked = 0;
gmmMarker.scale.x = sqrt(eigenvalues[0])*2;
gmmMarker.scale.y = sqrt(eigenvalues[1])*2;
gmmMarker.scale.z = sqrt(eigenvalues[2])*2;
// std::cout<<" scalex "<<gmmMarker.scale.x;
// std::cout<<" scaley "<<gmmMarker.scale.y;
// std::cout<<" scalez "<<gmmMarker.scale.z;
// std::cout<<std::endl;
// gmmMarker.scale.x = 0.02;
// gmmMarker.scale.y = 0.02;
// gmmMarker.scale.z = 0.02;
gmmMarker.color.a = gmm.weight*object.gmm.size()/2;
// gmmMarker.color.a = 1.0;
gmmMarker.color.r = ((double)r[id])/256;
gmmMarker.color.g = ((double)g[id])/256;
gmmMarker.color.b = ((double)b[id])/256;
// gmmMarker.color.r = 1.;
// gmmMarker.color.g = 1.;
// gmmMarker.color.b = 1.;
gmmMarkers.markers.push_back(gmmMarker);
}
// delete old marker
if(cnt < oldCnt){
for(int j=cnt+1;j<=oldCnt;j++){
visualization_msgs::Marker gmmMarker;
gmmMarker.header.frame_id = "/origin";
gmmMarker.header.stamp = ros::Time();
gmmMarker.ns = "gmm";
gmmMarker.id = j;
gmmMarker.type = visualization_msgs::Marker::SPHERE;
gmmMarker.action = visualization_msgs::Marker::DELETE;
gmmMarker.lifetime = ros::Duration(0);
gmmMarkers.markers.push_back(gmmMarker);
}
}
oldCnt = cnt;
// }
}
return gmmMarkers;
}
ON_NurbsSurface
FittingSurface::initNurbsPCABoundingBox (int order, NurbsDataSurface *m_data, Eigen::Vector3d z)
{
Eigen::Vector3d mean;
Eigen::Matrix3d eigenvectors;
Eigen::Vector3d eigenvalues;
unsigned s = m_data->interior.size ();
m_data->interior_param.clear ();
NurbsTools::pca (m_data->interior, mean, eigenvectors, eigenvalues);
m_data->mean = mean;
m_data->eigenvectors = eigenvectors;
bool flip (false);
if (eigenvectors.col (2).dot (z) < 0.0)
flip = true;
eigenvalues = eigenvalues / s; // seems that the eigenvalues are dependent on the number of points (???)
Eigen::Matrix3d eigenvectors_inv = eigenvectors.inverse ();
Eigen::Vector3d v_max (0.0, 0.0, 0.0);
Eigen::Vector3d v_min (DBL_MAX, DBL_MAX, DBL_MAX);
for (unsigned i = 0; i < s; i++)
{
Eigen::Vector3d p = eigenvectors_inv * (m_data->interior[i] - mean);
m_data->interior_param.push_back (Eigen::Vector2d (p (0), p (1)));
if (p (0) > v_max (0))
v_max (0) = p (0);
if (p (1) > v_max (1))
v_max (1) = p (1);
if (p (2) > v_max (2))
v_max (2) = p (2);
if (p (0) < v_min (0))
v_min (0) = p (0);
if (p (1) < v_min (1))
v_min (1) = p (1);
if (p (2) < v_min (2))
v_min (2) = p (2);
}
for (unsigned i = 0; i < s; i++)
{
Eigen::Vector2d &p = m_data->interior_param[i];
if (v_max (0) > v_min (0) && v_max (0) > v_min (0))
{
p (0) = (p (0) - v_min (0)) / (v_max (0) - v_min (0));
p (1) = (p (1) - v_min (1)) / (v_max (1) - v_min (1));
}
else
{
throw std::runtime_error ("[NurbsTools::initNurbsPCABoundingBox] Error: v_max <= v_min");
}
}
ON_NurbsSurface nurbs (3, false, order, order, order, order);
nurbs.MakeClampedUniformKnotVector (0, 1.0);
nurbs.MakeClampedUniformKnotVector (1, 1.0);
double dcu = (v_max (0) - v_min (0)) / (nurbs.Order (0) - 1);
double dcv = (v_max (1) - v_min (1)) / (nurbs.Order (1) - 1);
Eigen::Vector3d cv_t, cv;
for (int i = 0; i < nurbs.Order (0); i++)
{
for (int j = 0; j < nurbs.Order (1); j++)
{
cv (0) = v_min (0) + dcu * i;
cv (1) = v_min (1) + dcv * j;
cv (2) = 0.0;
cv_t = eigenvectors * cv + mean;
if (flip)
nurbs.SetCV (nurbs.Order (0) - 1 - i, j, ON_3dPoint (cv_t (0), cv_t (1), cv_t (2)));
else
nurbs.SetCV (i, j, ON_3dPoint (cv_t (0), cv_t (1), cv_t (2)));
}
}
return nurbs;
}
int main(int argc, char** argv) {
// ROS set-ups:
ros::init(argc, argv, "needle_planner_test_main"); //node name
ros::NodeHandle nh; // create a node handle; need to pass this to the class constructor
Eigen::Vector3d entrance_pt, exit_pt, tissue_normal;
tissue_normal << 0, 0, -1; //antiparallel to optical axis
entrance_pt << -0.1, 0.05, 0.1; //100mm under camera; slightly forward, to avoid jnt lims should be OK
exit_pt << -0.09, 0.05, 0.1; // exit pt is shifted along camera-frame +x axis relative to entrance pt
vector <Eigen::Affine3d> gripper_affines_wrt_camera; //put answers here
vector <Eigen::Affine3d> gripper2_affines_wrt_camera;
vector <Eigen::Affine3d> vetted_gripper_affines_wrt_camera;
ROS_INFO("instantiating forward solver and an ik_solver");
Davinci_fwd_solver davinci_fwd_solver; //instantiate a forward-kinematics solver
Davinci_IK_solver ik_solver;
ofstream outfile;
outfile.open ("best_poses.dat");
ROS_INFO("main: instantiating an object of type NeedlePlanner");
NeedlePlanner needlePlanner;
//compute the tissue frame in camera coords, based on point-cloud selections:
needlePlanner.compute_tissue_frame_wrt_camera(entrance_pt, exit_pt, tissue_normal);
//optional: manually set the needle grasp pose, else accept default from constructor
//needlePlanner.set_affine_needle_frame_wrt_gripper_frame(affine);
//optional: set the initial needle frame w/rt tissue frame (or accept default in constructor)
//needlePlanner.set_affine_needle_frame_wrt_tissue(Eigen::Affine3d affine)
// given grasp transform, tissue transform and initial needle pose w/rt tissue,
// compute needle-drive path as rotation about needle-z axis:
ROS_INFO("getting transforms from camera to PSMs");
tf::TransformListener tfListener;
tf::StampedTransform tfResult_one, tfResult_two;
Eigen::Affine3d affine_lcamera_to_psm_one, affine_lcamera_to_psm_two, affine_gripper_wrt_base;
bool tferr = true;
int ntries = 0;
ROS_INFO("waiting for tf between base and right_hand...");
while (tferr) {
if (ntries > 5) break; //give up and accept default after this many tries
tferr = false;
try {
//The direction of the transform returned will be from the target_frame to the source_frame.
//Which if applied to data, will transform data in the source_frame into the target_frame. See tf/CoordinateFrameConventions#Transform_Direction
tfListener.lookupTransform("left_camera_optical_frame", "one_psm_base_link", ros::Time(0), tfResult_one);
tfListener.lookupTransform("left_camera_optical_frame", "two_psm_base_link", ros::Time(0), tfResult_two);
} catch (tf::TransformException &exception) {
ROS_WARN("%s", exception.what());
tferr = true;
ros::Duration(0.5).sleep(); // sleep for half a second
ros::spinOnce();
ntries++;
}
}
//default transform: need to match this up to camera calibration!
if (tferr) {
affine_lcamera_to_psm_one.translation() << -0.155, -0.03265, 0.0;
Eigen::Vector3d nvec, tvec, bvec;
nvec << -1, 0, 0;
tvec << 0, 1, 0;
bvec << 0, 0, -1;
Eigen::Matrix3d R;
R.col(0) = nvec;
R.col(1) = tvec;
R.col(2) = bvec;
affine_lcamera_to_psm_one.linear() = R;
affine_lcamera_to_psm_two.linear() = R;
affine_lcamera_to_psm_two.translation() << 0.145, -0.03265, 0.0;
ROS_WARN("using default transform");
} else {
ROS_INFO("tf is good");
//affine_lcamera_to_psm_one is the position/orientation of psm1 base frame w/rt left camera link frame
// need to extend this to camera optical frame
affine_lcamera_to_psm_one = transformTFToEigen(tfResult_one);
affine_lcamera_to_psm_two = transformTFToEigen(tfResult_two);
}
ROS_INFO("transform from left camera to psm one:");
cout << affine_lcamera_to_psm_one.linear() << endl;
cout << affine_lcamera_to_psm_one.translation().transpose() << endl;
ROS_INFO("transform from left camera to psm two:");
cout << affine_lcamera_to_psm_two.linear() << endl;
cout << affine_lcamera_to_psm_two.translation().transpose() << endl;
//try grabbing needle w/ bvec_needle = -bvec_gripper
//needlePlanner.set_grab_needle_plus_minus_z(GRASP_W_NEEDLE_NEGATIVE_GRIPPER_Z);
//needlePlanner.compute_grasp_transform();
double ang_to_exit, phi_x, phi_y, tilt;
Eigen::Vector3d nvec_tissue;
vector <int> valid_ik_samps;
double best_phi_x = 0.0;
double best_phi_y = 0.0;
double best_tilt = 0.0;
int best_drive_score = 0;
int drive_score = 0;
int best_run = 0;
Eigen::VectorXi ik_ok_array(40); //Add by DLC 5-26-2016 also in test_main, test_main_v2
int ik_score=0;
//while (ros::ok())
{
cout << "entrance point at: " << entrance_pt.transpose() << endl;
cout << "enter angle to exit point (0-2pi): ";
cin >> ang_to_exit;
//cout << "enter needle-grasp phi_x: ";
//cin >> phi_x;
for (phi_x = 0.0; phi_x < 6.28; phi_x += 0.1) {
for (phi_y = 0.0; phi_y < 6.28; phi_y += 0.1) {
ROS_INFO("phi_x, phi_y = %f, %f",phi_x,phi_y);
for (tilt = -1.2; tilt < 1.21; tilt += 0.1) {
drive_score = 0;
//cout << "enter needle-grasp phi_y: ";
//cin >> phi_y;
needlePlanner.compute_grasp_transform(phi_x, phi_y);
//cout<< "enter tilt of needle z-axis w/rt tissue: ";
//cin>> tilt;
needlePlanner.set_psi_needle_axis_tilt_wrt_tissue(tilt);
//for (ang_to_exit = 0; ang_to_exit < 6.28; ang_to_exit += 1.0)
{
//cout << "angle to exit pt on tissue surface: " << ang_to_exit << endl;
//cin>>ang_to_exit;
nvec_tissue << cos(ang_to_exit), sin(ang_to_exit), 0.0;
exit_pt = entrance_pt + nvec_tissue;
needlePlanner.compute_tissue_frame_wrt_camera(entrance_pt, exit_pt, tissue_normal);
gripper_affines_wrt_camera.clear();
gripper2_affines_wrt_camera.clear();
vetted_gripper_affines_wrt_camera.clear();
needlePlanner.compute_needle_drive_gripper_affines(gripper_affines_wrt_camera, gripper2_affines_wrt_camera, ik_ok_array, ik_score);
int nposes = gripper_affines_wrt_camera.size();
//ROS_INFO("computed %d gripper poses w/rt camera", nposes);
Eigen::Affine3d affine_pose, affine_gripper_wrt_base_frame, affine_gripper_wrt_base_fk;
Eigen::Vector3d origin_err;
Eigen::Matrix3d R_err;
Vectorq7x1 q_vec1;
q_vec1.resize(7);
valid_ik_samps.clear();
for (int i = 0; i < nposes; i++) {
if (test_debug) ROS_INFO("pose %d", i);
affine_pose = gripper_affines_wrt_camera[i];
if (test_debug) cout << affine_pose.linear() << endl;
if (test_debug) cout << "origin: " << affine_pose.translation().transpose() << endl;
affine_gripper_wrt_base_frame = affine_lcamera_to_psm_one.inverse() * affine_pose;
if (test_debug) ROS_INFO("pose %d w/rt PSM1 base:", i);
if (test_debug) cout << affine_gripper_wrt_base_frame.linear() << endl;
if (test_debug) cout << "origin: " << affine_gripper_wrt_base_frame.translation().transpose() << endl;
if (ik_solver.ik_solve(affine_gripper_wrt_base_frame)) { //convert desired pose into equiv joint displacements
valid_ik_samps.push_back(1);
drive_score++;
vetted_gripper_affines_wrt_camera.push_back(affine_pose); //accept this one
q_vec1 = ik_solver.get_soln();
q_vec1(6) = 0.0;
if (test_debug) cout << "qvec1: " << q_vec1.transpose() << endl;
if (test_debug) ROS_INFO("FK of IK soln: ");
affine_gripper_wrt_base_fk = davinci_fwd_solver.fwd_kin_solve(q_vec1);
if (test_debug) cout << "affine linear (R): " << endl;
if (test_debug) cout << affine_gripper_wrt_base_fk.linear() << endl;
if (test_debug) cout << "origin: ";
if (test_debug) cout << affine_gripper_wrt_base_fk.translation().transpose() << endl;
origin_err = affine_gripper_wrt_base_frame.translation() - affine_gripper_wrt_base_fk.translation();
R_err = affine_gripper_wrt_base_frame.linear() - affine_gripper_wrt_base_fk.linear();
if (test_debug) ROS_WARN("error test: %f", origin_err.norm());
if (test_debug) cout << "IK/FK origin err: " << origin_err.transpose() << endl;
if (test_debug) cout << "R err: " << endl;
if (test_debug) cout << R_err << endl;
} else {
if (test_debug) ROS_WARN("GRIPPER 1: NO VALID IK SOLN!!");
valid_ik_samps.push_back(0);
}
} //end loop through needle-drive poses
drive_score = runlength_ones(valid_ik_samps);
ROS_INFO("drive_score: %d",drive_score);
} //end loop ang_to_exit
if (drive_score == best_drive_score) {
best_drive_score = drive_score;
best_phi_x = phi_x;
best_phi_y = phi_y;
best_tilt = tilt;
cout << "valid samples status:" << endl;
for (int i = 0; i < valid_ik_samps.size(); i++) {
cout << valid_ik_samps[i] << " ";
}
cout << endl;
ROS_INFO("phi_x, phi_y, tilt = %f, %f, %f",phi_x,phi_y,tilt);
ROS_WARN("tied best strategy; score = %d; enter 1: ",best_drive_score);
int ans;
//cin>>ans;
//needlePlanner.write_needle_drive_affines_to_file(vetted_gripper_affines_wrt_camera);
}
if (drive_score > best_drive_score) {
best_drive_score = drive_score;
best_phi_x = phi_x;
best_phi_y = phi_y;
best_tilt = tilt;
cout << "valid samples status:" << endl;
for (int i = 0; i < valid_ik_samps.size(); i++) {
cout << valid_ik_samps[i] << " ";
}
cout << endl;
ROS_INFO("phi_x, phi_y, tilt = %f, %f, %f",phi_x,phi_y,tilt);
ROS_WARN("new best strategy; score = %d; enter 1: ",best_drive_score);
int ans;
//cin>>ans;
needlePlanner.write_needle_drive_affines_to_file(vetted_gripper_affines_wrt_camera);
}
if (drive_score==21) { // save best solns to file
outfile<<phi_x<<", "<<phi_y<<", "<<tilt<<endl;
}
}
}
}
}
//needlePlanner.write_needle_drive_affines_to_file(vetted_gripper_affines_wrt_camera);
ROS_INFO("best drive score: %d", best_drive_score);
ROS_INFO("best phi_x, phi_y, tilt = %f, %f, %f", best_phi_x, best_phi_y, best_tilt);
outfile.close();
return 0;
}
int main(int argc, char** argv)
{
ros::init(argc, argv, "vision_guided_motion_action_client");
ros::NodeHandle nh;
// create an instance of arm commander class and cwru_pcl_utils class
ArmMotionCommander arm_motion_commander(&nh);
CwruPclUtils cwru_pcl_utils(&nh);
// wait to receive point cloud
while (!cwru_pcl_utils.got_kinect_cloud())
{
ROS_INFO("did not receive pointcloud");
ros::spinOnce();
ros::Duration(1.0).sleep();
}
ROS_INFO("got a pointcloud");
tf::StampedTransform tf_sensor_frame_to_torso_frame; // use this to transform sensor frame to torso frame
tf::TransformListener tf_listener; // start a transform listener
// warm up the tf_listener (this is to make sure it get's all the transforms it needs to avoid crashing)
bool tferr = true;
ROS_INFO("waiting for tf between kinect_pc_frame and torso...");
while (tferr)
{
tferr = false;
try
{
// The direction of the transform returned will be from the target_frame to the source_frame
// which if applied to data, will transform data in the source_frame into the target_frame
// See tf/CoordinateFrameConventions#Transform_Direction
tf_listener.lookupTransform("torso", "kinect_pc_frame", ros::Time(0), tf_sensor_frame_to_torso_frame);
}
catch (tf::TransformException &exception)
{
ROS_ERROR("%s", exception.what());
tferr = true;
ros::Duration(0.5).sleep();
ros::spinOnce();
}
}
ROS_INFO("tf is good"); // tf_listener found a complete chain from sensor to world
Eigen::Affine3f A_sensor_wrt_torso;
Eigen::Affine3d Affine_des_gripper;
Eigen::Vector3d xvec_des, yvec_des, zvec_des, origin_des;
geometry_msgs::PoseStamped rt_tool_pose;
Eigen::Vector3f plane_normal, major_axis, centroid;
Eigen::Matrix3d Rmat;
int rtn_val;
double plane_dist;
// convert the tf to an Eigen::Affine
A_sensor_wrt_torso = cwru_pcl_utils.transformTFToEigen(tf_sensor_frame_to_torso_frame);
// send a command to plan and then execute a joint-space move to pre-defined pose
rtn_val = arm_motion_commander.plan_move_to_pre_pose();
rtn_val = arm_motion_commander.rt_arm_execute_planned_path();
// repeatedly scan for a new patch of selected points
while (ros::ok())
{
if (cwru_pcl_utils.got_selected_points())
{
ROS_INFO("transforming selected points");
cwru_pcl_utils.transform_selected_points_cloud(A_sensor_wrt_torso);
// fit a plane to these selected points
cwru_pcl_utils.fit_xformed_selected_pts_to_plane(plane_normal, plane_dist);
ROS_INFO_STREAM("normal: " << plane_normal.transpose() << "\ndist = " << plane_dist);
major_axis = cwru_pcl_utils.get_major_axis();
centroid = cwru_pcl_utils.get_centroid();
cwru_pcl_utils.reset_got_selected_points(); // reset the selected-points trigger
// construct a goal affine pose
for (int i = 0; i < 3; i++) {
origin_des[i] = centroid[i]; // convert to double precision
zvec_des[i] = -plane_normal[i]; // want tool z pointing OPPOSITE surface normal
xvec_des[i] = major_axis[i];
}
origin_des[2] += 0.02; // raise up 2cm
yvec_des = zvec_des.cross(xvec_des); // construct consistent right-hand triad
Rmat.col(0)= xvec_des;
Rmat.col(1)= yvec_des;
Rmat.col(2)= zvec_des;
Affine_des_gripper.linear() = Rmat;
Affine_des_gripper.translation() = origin_des;
ROS_INFO_STREAM("des origin: " << Affine_des_gripper.translation().transpose() << endl);
ROS_INFO_STREAM("orientation: " << endl);
ROS_INFO_STREAM(Rmat << endl);
// convert des pose from Eigen::Affine to geometry_msgs::PoseStamped
rt_tool_pose.pose = arm_motion_commander.transformEigenAffine3dToPose(Affine_des_gripper);
// send move plan request
rtn_val = arm_motion_commander.rt_arm_plan_path_current_to_goal_pose(rt_tool_pose);
if (rtn_val == cwru_action::cwru_baxter_cart_moveResult::SUCCESS) {
// send command to execute planned motion
rtn_val = arm_motion_commander.rt_arm_execute_planned_path();
/* New code added to enable wiping motion */
geometry_msgs::PoseStamped rt_tool_wipe_table;
origin_des[1] += 0.1;
Affine_des_gripper.translation() = origin_des;
rt_tool_wipe_table.pose = arm_motion_commander.transformEigenAffine3dToPose(Affine_des_gripper);
rtn_val = arm_motion_commander.rt_arm_plan_path_current_to_goal_pose(rt_tool_wipe_table);
if (rtn_val == cwru_action::cwru_baxter_cart_moveResult::SUCCESS) {
rtn_val = arm_motion_commander.rt_arm_execute_planned_path();
} else {
ROS_WARN("Cartesian path to desired pose not achievable");
}
origin_des[1] -= 0.2;
Affine_des_gripper.translation() = origin_des;
rt_tool_wipe_table.pose = arm_motion_commander.transformEigenAffine3dToPose(Affine_des_gripper);
rtn_val = arm_motion_commander.rt_arm_plan_path_current_to_goal_pose(rt_tool_wipe_table);
if (rtn_val == cwru_action::cwru_baxter_cart_moveResult::SUCCESS){
rtn_val = arm_motion_commander.rt_arm_execute_planned_path();
} else {
ROS_WARN("Cartesian path to desired pose not achievable");
}
origin_des[1] += 0.2;
Affine_des_gripper.translation() = origin_des;
rt_tool_wipe_table.pose = arm_motion_commander.transformEigenAffine3dToPose(Affine_des_gripper);
rtn_val = arm_motion_commander.rt_arm_plan_path_current_to_goal_pose(rt_tool_wipe_table);
if (rtn_val == cwru_action::cwru_baxter_cart_moveResult::SUCCESS){
rtn_val = arm_motion_commander.rt_arm_execute_planned_path();
} else {
ROS_WARN("Cartesian path to desired pose not achievable");
}
origin_des[1] -= 0.1;
Affine_des_gripper.translation() = origin_des;
rt_tool_wipe_table.pose = arm_motion_commander.transformEigenAffine3dToPose(Affine_des_gripper);
rtn_val = arm_motion_commander.rt_arm_plan_path_current_to_goal_pose(rt_tool_wipe_table);
if (rtn_val == cwru_action::cwru_baxter_cart_moveResult::SUCCESS){
rtn_val = arm_motion_commander.rt_arm_execute_planned_path();
} else {
ROS_WARN("Cartesian path to desired pose not achievable");
}
/* end of new code */
} else {
ROS_WARN("Cartesian path to desired pose not achievable");
}
}
ros::Duration(0.5).sleep(); // sleep for half a second
ros::spinOnce();
}
return 0;
}
