gicp.hpp
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#ifndef PCL_REGISTRATION_IMPL_GICP_HPP_
#define PCL_REGISTRATION_IMPL_GICP_HPP_
#include <pcl/registration/exceptions.h>
namespace pcl {
template <typename PointSource, typename PointTarget>
template <typename PointT>
void
GeneralizedIterativeClosestPoint<PointSource, PointTarget>::computeCovariances(
typename pcl::PointCloud<PointT>::ConstPtr cloud,
const typename pcl::search::KdTree<PointT>::Ptr kdtree,
MatricesVector& 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;
pcl::Indices 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());
MatricesVector::iterator matrices_iterator = cloud_covariances.begin();
for (auto points_iterator = cloud->begin(); 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();
}
}
}
template <typename PointSource, typename PointTarget>
void
GeneralizedIterativeClosestPoint<PointSource, PointTarget>::computeRDerivative(
const Vector6d& x, const Eigen::Matrix3d& R, Vector6d& g) const
{
Eigen::Matrix3d dR_dPhi;
Eigen::Matrix3d dR_dTheta;
Eigen::Matrix3d dR_dPsi;
double phi = x[3], theta = x[4], psi = x[5];
double cphi = std::cos(phi), sphi = sin(phi);
double ctheta = std::cos(theta), stheta = sin(theta);
double cpsi = std::cos(psi), spsi = sin(psi);
dR_dPhi(0, 0) = 0.;
dR_dPhi(1, 0) = 0.;
dR_dPhi(2, 0) = 0.;
dR_dPhi(0, 1) = sphi * spsi + cphi * cpsi * stheta;
dR_dPhi(1, 1) = -cpsi * sphi + cphi * spsi * stheta;
dR_dPhi(2, 1) = cphi * ctheta;
dR_dPhi(0, 2) = cphi * spsi - cpsi * sphi * stheta;
dR_dPhi(1, 2) = -cphi * cpsi - sphi * spsi * stheta;
dR_dPhi(2, 2) = -ctheta * sphi;
dR_dTheta(0, 0) = -cpsi * stheta;
dR_dTheta(1, 0) = -spsi * stheta;
dR_dTheta(2, 0) = -ctheta;
dR_dTheta(0, 1) = cpsi * ctheta * sphi;
dR_dTheta(1, 1) = ctheta * sphi * spsi;
dR_dTheta(2, 1) = -sphi * stheta;
dR_dTheta(0, 2) = cphi * cpsi * ctheta;
dR_dTheta(1, 2) = cphi * ctheta * spsi;
dR_dTheta(2, 2) = -cphi * stheta;
dR_dPsi(0, 0) = -ctheta * spsi;
dR_dPsi(1, 0) = cpsi * ctheta;
dR_dPsi(2, 0) = 0.;
dR_dPsi(0, 1) = -cphi * cpsi - sphi * spsi * stheta;
dR_dPsi(1, 1) = -cphi * spsi + cpsi * sphi * stheta;
dR_dPsi(2, 1) = 0.;
dR_dPsi(0, 2) = cpsi * sphi - cphi * spsi * stheta;
dR_dPsi(1, 2) = sphi * spsi + cphi * cpsi * stheta;
dR_dPsi(2, 2) = 0.;
g[3] = matricesInnerProd(dR_dPhi, R);
g[4] = matricesInnerProd(dR_dTheta, R);
g[5] = matricesInnerProd(dR_dPsi, R);
}
template <typename PointSource, typename PointTarget>
void
GeneralizedIterativeClosestPoint<PointSource, PointTarget>::
estimateRigidTransformationBFGS(const PointCloudSource& cloud_src,
const pcl::Indices& indices_src,
const PointCloudTarget& cloud_tgt,
const pcl::Indices& indices_tgt,
Eigen::Matrix4f& transformation_matrix)
{
if (indices_src.size() < 4) // need at least 4 samples
{
PCL_THROW_EXCEPTION(
NotEnoughPointsException,
"[pcl::GeneralizedIterativeClosestPoint::estimateRigidTransformationBFGS] Need "
"at least 4 points to estimate a transform! Source and target have "
<< indices_src.size() << " points!");
return;
}
// Set the initial solution
Vector6d x = Vector6d::Zero();
// translation part
x[0] = transformation_matrix(0, 3);
x[1] = transformation_matrix(1, 3);
x[2] = transformation_matrix(2, 3);
// rotation part (Z Y X euler angles convention)
// see: https://en.wikipedia.org/wiki/Rotation_matrix#General_rotations
x[3] = std::atan2(transformation_matrix(2, 1), transformation_matrix(2, 2));
x[4] = asin(-transformation_matrix(2, 0));
x[5] = std::atan2(transformation_matrix(1, 0), transformation_matrix(0, 0));
// Set temporary pointers
tmp_src_ = &cloud_src;
tmp_tgt_ = &cloud_tgt;
tmp_idx_src_ = &indices_src;
tmp_idx_tgt_ = &indices_tgt;
// Optimize using forward-difference approximation LM
OptimizationFunctorWithIndices functor(this);
BFGS<OptimizationFunctorWithIndices> bfgs(functor);
bfgs.parameters.sigma = 0.01;
bfgs.parameters.rho = 0.01;
bfgs.parameters.tau1 = 9;
bfgs.parameters.tau2 = 0.05;
bfgs.parameters.tau3 = 0.5;
bfgs.parameters.order = 3;
int inner_iterations_ = 0;
int result = bfgs.minimizeInit(x);
result = BFGSSpace::Running;
do {
inner_iterations_++;
result = bfgs.minimizeOneStep(x);
if (result) {
break;
}
result = bfgs.testGradient();
} while (result == BFGSSpace::Running && inner_iterations_ < max_inner_iterations_);
if (result == BFGSSpace::NoProgress || result == BFGSSpace::Success ||
inner_iterations_ == max_inner_iterations_) {
PCL_DEBUG("[pcl::registration::TransformationEstimationBFGS::"
"estimateRigidTransformation]");
PCL_DEBUG("BFGS solver finished with exit code %i \n", result);
transformation_matrix.setIdentity();
applyState(transformation_matrix, x);
}
else
PCL_THROW_EXCEPTION(
SolverDidntConvergeException,
"[pcl::" << getClassName()
<< "::TransformationEstimationBFGS::estimateRigidTransformation] BFGS "
"solver didn't converge!");
}
template <typename PointSource, typename PointTarget>
inline double
GeneralizedIterativeClosestPoint<PointSource, PointTarget>::
OptimizationFunctorWithIndices::operator()(const Vector6d& x)
{
Eigen::Matrix4f transformation_matrix = gicp_->base_transformation_;
gicp_->applyState(transformation_matrix, x);
double f = 0;
int m = static_cast<int>(gicp_->tmp_idx_src_->size());
for (int i = 0; i < m; ++i) {
// The last coordinate, p_src[3] is guaranteed to be set to 1.0 in registration.hpp
Vector4fMapConst p_src =
(*gicp_->tmp_src_)[(*gicp_->tmp_idx_src_)[i]].getVector4fMap();
// The last coordinate, p_tgt[3] is guaranteed to be set to 1.0 in registration.hpp
Vector4fMapConst p_tgt =
(*gicp_->tmp_tgt_)[(*gicp_->tmp_idx_tgt_)[i]].getVector4fMap();
Eigen::Vector4f pp(transformation_matrix * p_src);
// Estimate the distance (cost function)
// The last coordinate is still guaranteed to be set to 1.0
Eigen::Vector3d res(pp[0] - p_tgt[0], pp[1] - p_tgt[1], pp[2] - p_tgt[2]);
Eigen::Vector3d temp(gicp_->mahalanobis((*gicp_->tmp_idx_src_)[i]) * res);
// increment= res'*temp/num_matches = temp'*M*temp/num_matches (we postpone
// 1/num_matches after the loop closes)
f += double(res.transpose() * temp);
}
return f / m;
}
template <typename PointSource, typename PointTarget>
inline void
GeneralizedIterativeClosestPoint<PointSource, PointTarget>::
OptimizationFunctorWithIndices::df(const Vector6d& x, Vector6d& g)
{
Eigen::Matrix4f transformation_matrix = gicp_->base_transformation_;
gicp_->applyState(transformation_matrix, x);
// Zero out g
g.setZero();
// Eigen::Vector3d g_t = g.head<3> ();
Eigen::Matrix3d R = Eigen::Matrix3d::Zero();
int m = static_cast<int>(gicp_->tmp_idx_src_->size());
for (int i = 0; i < m; ++i) {
// The last coordinate, p_src[3] is guaranteed to be set to 1.0 in registration.hpp
Vector4fMapConst p_src =
(*gicp_->tmp_src_)[(*gicp_->tmp_idx_src_)[i]].getVector4fMap();
// The last coordinate, p_tgt[3] is guaranteed to be set to 1.0 in registration.hpp
Vector4fMapConst p_tgt =
(*gicp_->tmp_tgt_)[(*gicp_->tmp_idx_tgt_)[i]].getVector4fMap();
Eigen::Vector4f pp(transformation_matrix * p_src);
// The last coordinate is still guaranteed to be set to 1.0
Eigen::Vector3d res(pp[0] - p_tgt[0], pp[1] - p_tgt[1], pp[2] - p_tgt[2]);
// temp = M*res
Eigen::Vector3d temp(gicp_->mahalanobis((*gicp_->tmp_idx_src_)[i]) * res);
// Increment translation gradient
// g.head<3> ()+= 2*M*res/num_matches (we postpone 2/num_matches after the loop
// closes)
g.head<3>() += temp;
// Increment rotation gradient
pp = gicp_->base_transformation_ * p_src;
Eigen::Vector3d p_src3(pp[0], pp[1], pp[2]);
R += p_src3 * temp.transpose();
}
g.head<3>() *= 2.0 / m;
R *= 2.0 / m;
gicp_->computeRDerivative(x, R, g);
}
template <typename PointSource, typename PointTarget>
inline void
GeneralizedIterativeClosestPoint<PointSource, PointTarget>::
OptimizationFunctorWithIndices::fdf(const Vector6d& x, double& f, Vector6d& g)
{
Eigen::Matrix4f transformation_matrix = gicp_->base_transformation_;
gicp_->applyState(transformation_matrix, x);
f = 0;
g.setZero();
Eigen::Matrix3d R = Eigen::Matrix3d::Zero();
const int m = static_cast<int>(gicp_->tmp_idx_src_->size());
for (int i = 0; i < m; ++i) {
// The last coordinate, p_src[3] is guaranteed to be set to 1.0 in registration.hpp
Vector4fMapConst p_src =
(*gicp_->tmp_src_)[(*gicp_->tmp_idx_src_)[i]].getVector4fMap();
// The last coordinate, p_tgt[3] is guaranteed to be set to 1.0 in registration.hpp
Vector4fMapConst p_tgt =
(*gicp_->tmp_tgt_)[(*gicp_->tmp_idx_tgt_)[i]].getVector4fMap();
Eigen::Vector4f pp(transformation_matrix * p_src);
// The last coordinate is still guaranteed to be set to 1.0
Eigen::Vector3d res(pp[0] - p_tgt[0], pp[1] - p_tgt[1], pp[2] - p_tgt[2]);
// temp = M*res
Eigen::Vector3d temp(gicp_->mahalanobis((*gicp_->tmp_idx_src_)[i]) * res);
// Increment total error
f += double(res.transpose() * temp);
// Increment translation gradient
// g.head<3> ()+= 2*M*res/num_matches (we postpone 2/num_matches after the loop
// closes)
g.head<3>() += temp;
pp = gicp_->base_transformation_ * p_src;
Eigen::Vector3d p_src3(pp[0], pp[1], pp[2]);
// Increment rotation gradient
R += p_src3 * temp.transpose();
}
f /= double(m);
g.head<3>() *= double(2.0 / m);
R *= 2.0 / m;
gicp_->computeRDerivative(x, R, g);
}
template <typename PointSource, typename PointTarget>
inline BFGSSpace::Status
GeneralizedIterativeClosestPoint<PointSource, PointTarget>::
OptimizationFunctorWithIndices::checkGradient(const Vector6d& g)
{
auto translation_epsilon = gicp_->translation_gradient_tolerance_;
auto rotation_epsilon = gicp_->rotation_gradient_tolerance_;
if ((translation_epsilon < 0.) || (rotation_epsilon < 0.))
return BFGSSpace::NegativeGradientEpsilon;
// express translation gradient as norm of translation parameters
auto translation_grad = g.head<3>().norm();
// express rotation gradient as a norm of rotation parameters
auto rotation_grad = g.tail<3>().norm();
if ((translation_grad < translation_epsilon) && (rotation_grad < rotation_epsilon))
return BFGSSpace::Success;
return BFGSSpace::Running;
}
template <typename PointSource, typename PointTarget>
inline void
GeneralizedIterativeClosestPoint<PointSource, PointTarget>::computeTransformation(
PointCloudSource& output, const Eigen::Matrix4f& guess)
{
pcl::IterativeClosestPoint<PointSource, PointTarget>::initComputeReciprocal();
// Difference between consecutive transforms
double delta = 0;
// Get the size of the target
const std::size_t N = indices_->size();
// Set the mahalanobis matrices to identity
mahalanobis_.resize(N, Eigen::Matrix3d::Identity());
// Compute target cloud covariance matrices
if ((!target_covariances_) || (target_covariances_->empty())) {
target_covariances_.reset(new MatricesVector);
computeCovariances<PointTarget>(target_, tree_, *target_covariances_);
}
// Compute input cloud covariance matrices
if ((!input_covariances_) || (input_covariances_->empty())) {
input_covariances_.reset(new MatricesVector);
computeCovariances<PointSource>(input_, tree_reciprocal_, *input_covariances_);
}
base_transformation_ = Eigen::Matrix4f::Identity();
nr_iterations_ = 0;
converged_ = false;
double dist_threshold = corr_dist_threshold_ * corr_dist_threshold_;
pcl::Indices nn_indices(1);
std::vector<float> nn_dists(1);
pcl::transformPointCloud(output, output, guess);
while (!converged_) {
std::size_t cnt = 0;
pcl::Indices source_indices(indices_->size());
pcl::Indices target_indices(indices_->size());
// guess corresponds to base_t and transformation_ to t
Eigen::Matrix4d transform_R = Eigen::Matrix4d::Zero();
for (std::size_t i = 0; i < 4; i++)
for (std::size_t j = 0; j < 4; j++)
for (std::size_t k = 0; k < 4; k++)
transform_R(i, j) += double(transformation_(i, k)) * double(guess(k, j));
Eigen::Matrix3d R = transform_R.topLeftCorner<3, 3>();
for (std::size_t i = 0; i < N; i++) {
PointSource query = output[i];
query.getVector4fMap() = transformation_ * query.getVector4fMap();
if (!searchForNeighbors(query, nn_indices, nn_dists)) {
PCL_ERROR("[pcl::%s::computeTransformation] Unable to find a nearest neighbor "
"in the target dataset for point %d in the source!\n",
getClassName().c_str(),
(*indices_)[i]);
return;
}
// Check if the distance to the nearest neighbor is smaller than the user imposed
// threshold
if (nn_dists[0] < dist_threshold) {
Eigen::Matrix3d& C1 = (*input_covariances_)[i];
Eigen::Matrix3d& C2 = (*target_covariances_)[nn_indices[0]];
Eigen::Matrix3d& M = mahalanobis_[i];
// M = R*C1
M = R * C1;
// temp = M*R' + C2 = R*C1*R' + C2
Eigen::Matrix3d temp = M * R.transpose();
temp += C2;
// M = temp^-1
M = temp.inverse();
source_indices[cnt] = static_cast<int>(i);
target_indices[cnt] = nn_indices[0];
cnt++;
}
}
// Resize to the actual number of valid correspondences
source_indices.resize(cnt);
target_indices.resize(cnt);
/* optimize transformation using the current assignment and Mahalanobis metrics*/
previous_transformation_ = transformation_;
// optimization right here
try {
rigid_transformation_estimation_(
output, source_indices, *target_, target_indices, transformation_);
/* compute the delta from this iteration */
delta = 0.;
for (int k = 0; k < 4; k++) {
for (int l = 0; l < 4; l++) {
double ratio = 1;
if (k < 3 && l < 3) // rotation part of the transform
ratio = 1. / rotation_epsilon_;
else
ratio = 1. / transformation_epsilon_;
double c_delta =
ratio * std::abs(previous_transformation_(k, l) - transformation_(k, l));
if (c_delta > delta)
delta = c_delta;
}
}
} catch (PCLException& e) {
PCL_DEBUG("[pcl::%s::computeTransformation] Optimization issue %s\n",
getClassName().c_str(),
e.what());
break;
}
nr_iterations_++;
// Check for convergence
if (nr_iterations_ >= max_iterations_ || delta < 1) {
converged_ = true;
PCL_DEBUG("[pcl::%s::computeTransformation] Convergence reached. Number of "
"iterations: %d out of %d. Transformation difference: %f\n",
getClassName().c_str(),
nr_iterations_,
max_iterations_,
(transformation_ - previous_transformation_).array().abs().sum());
previous_transformation_ = transformation_;
}
else
PCL_DEBUG("[pcl::%s::computeTransformation] Convergence failed\n",
getClassName().c_str());
}
final_transformation_ = previous_transformation_ * guess;
// Transform the point cloud
pcl::transformPointCloud(*input_, output, final_transformation_);
}
template <typename PointSource, typename PointTarget>
void
GeneralizedIterativeClosestPoint<PointSource, PointTarget>::applyState(
Eigen::Matrix4f& t, const Vector6d& x) const
{
// Z Y X euler angles convention
Eigen::Matrix3f R;
R = Eigen::AngleAxisf(static_cast<float>(x[5]), Eigen::Vector3f::UnitZ()) *
Eigen::AngleAxisf(static_cast<float>(x[4]), Eigen::Vector3f::UnitY()) *
Eigen::AngleAxisf(static_cast<float>(x[3]), Eigen::Vector3f::UnitX());
t.topLeftCorner<3, 3>().matrix() = R * t.topLeftCorner<3, 3>().matrix();
Eigen::Vector4f T(static_cast<float>(x[0]),
static_cast<float>(x[1]),
static_cast<float>(x[2]),
0.0f);
t.col(3) += T;
}
} // namespace pcl
#endif // PCL_REGISTRATION_IMPL_GICP_HPP_
BFGS
BFGS stands for Broyden–Fletcher–Goldfarb–Shanno (BFGS) method for solving unconstrained nonlinear op...
Definition: bfgs.h:121
pcl::GeneralizedIterativeClosestPoint::computeCovariances
void computeCovariances(typename pcl::PointCloud< PointT >::ConstPtr cloud, const typename pcl::search::KdTree< PointT >::Ptr tree, MatricesVector &cloud_covariances)
compute points covariances matrices according to the K nearest neighbors.
Definition: gicp.hpp:51
pcl::GeneralizedIterativeClosestPoint::applyState
void applyState(Eigen::Matrix4f &t, const Vector6d &x) const
compute transformation matrix from transformation matrix
Definition: gicp.hpp:518
pcl::GeneralizedIterativeClosestPoint::MatricesVector
std::vector< Eigen::Matrix3d, Eigen::aligned_allocator< Eigen::Matrix3d > > MatricesVector
Definition: gicp.h:92
pcl::GeneralizedIterativeClosestPoint::computeTransformation
void computeTransformation(PointCloudSource &output, const Eigen::Matrix4f &guess) override
Rigid transformation computation method with initial guess.
Definition: gicp.hpp:390
pcl::GeneralizedIterativeClosestPoint::computeRDerivative
void computeRDerivative(const Vector6d &x, const Eigen::Matrix3d &R, Vector6d &g) const
Computes rotation matrix derivative.
Definition: gicp.hpp:131
pcl::GeneralizedIterativeClosestPoint::estimateRigidTransformationBFGS
void estimateRigidTransformationBFGS(const PointCloudSource &cloud_src, const pcl::Indices &indices_src, const PointCloudTarget &cloud_tgt, const pcl::Indices &indices_tgt, Eigen::Matrix4f &transformation_matrix)
Estimate a rigid rotation transformation between a source and a target point cloud using an iterative...
Definition: gicp.hpp:188
pcl::GeneralizedIterativeClosestPoint::Vector6d
Eigen::Matrix< double, 6, 1 > Vector6d
Definition: gicp.h:103
pcl::NotEnoughPointsException
An exception that is thrown when the number of correspondents is not equal to the minimum required.
Definition: exceptions.h:63
pcl::PointCloud< PointSource >
pcl::PointCloud::end
iterator end() noexcept
Definition: point_cloud.h:430
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:443
pcl::PointCloud::begin
iterator begin() noexcept
Definition: point_cloud.h:429
pcl::PointCloud::ConstPtr
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:414
pcl::Registration< PointSource, PointTarget, float >::initComputeReciprocal
bool initComputeReciprocal()
Internal computation when reciprocal lookup is needed.
Definition: registration.hpp:105
pcl::search::KdTree::nearestKSearch
int nearestKSearch(const PointT &point, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
Search for the k-nearest neighbors for the given query point.
Definition: kdtree.hpp:87
pcl::search::KdTree::Ptr
shared_ptr< KdTree< PointT, Tree > > Ptr
Definition: kdtree.h:75
pcl::transformPointCloud
void transformPointCloud(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform, bool copy_all_fields)
Apply a rigid transform defined by a 4x4 matrix.
Definition: transforms.hpp:221
BFGSSpace::Status
Status
Definition: bfgs.h:70
BFGSSpace::NoProgress
@ NoProgress
Definition: bfgs.h:75
BFGSSpace::Running
@ Running
Definition: bfgs.h:73
BFGSSpace::Success
@ Success
Definition: bfgs.h:74
BFGSSpace::NegativeGradientEpsilon
@ NegativeGradientEpsilon
Definition: bfgs.h:71
pcl
Definition: convolution.h:46
pcl::Vector4fMapConst
const Eigen::Map< const Eigen::Vector4f, Eigen::Aligned > Vector4fMapConst
Definition: point_types.hpp:229
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
pcl::GeneralizedIterativeClosestPoint::OptimizationFunctorWithIndices::checkGradient
BFGSSpace::Status checkGradient(const Vector6d &g) override
Definition: gicp.hpp:368
pcl::GeneralizedIterativeClosestPoint::OptimizationFunctorWithIndices::df
void df(const Vector6d &x, Vector6d &df) override
Definition: gicp.hpp:289
pcl::GeneralizedIterativeClosestPoint::OptimizationFunctorWithIndices::operator()
double operator()(const Vector6d &x) override
Definition: gicp.hpp:261
pcl::GeneralizedIterativeClosestPoint::OptimizationFunctorWithIndices::fdf
void fdf(const Vector6d &x, double &f, Vector6d &df) override
Definition: gicp.hpp:328
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:675