一、pcl中的非线性迭代
pcl中L-M源码
* POSSIBILITY OF SUCH DAMAGE.
*
* $Id$
*
*/
#ifndef PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_LM_HPP_
#define PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_LM_HPP_
#include
#include
#include
#include
//
template
pcl::registration::TransformationEstimationLM::TransformationEstimationLM ()
: tmp_src_ ()
, tmp_tgt_ ()
, tmp_idx_src_ ()
, tmp_idx_tgt_ ()
, warp_point_ (new WarpPointRigid6D)
{
};
//
template void
pcl::registration::TransformationEstimationLM::estimateRigidTransformation (
const pcl::PointCloud &cloud_src,
const pcl::PointCloud &cloud_tgt,
Matrix4 &transformation_matrix) const
{
// is the source dataset
if (cloud_src.points.size () != cloud_tgt.points.size ())
{
PCL_ERROR ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] ");
PCL_ERROR ("Number or points in source (%lu) differs than target (%lu)!\n",
cloud_src.points.size (), cloud_tgt.points.size ());
return;
}
if (cloud_src.points.size () < 4) // need at least 4 samples
{
PCL_ERROR ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] ");
PCL_ERROR ("Need at least 4 points to estimate a transform! Source and target have %lu points!\n",
cloud_src.points.size ());
return;
}
int n_unknowns = warp_point_->getDimension ();
VectorX x (n_unknowns);
x.setZero ();
// Set temporary pointers
tmp_src_ = &cloud_src;
tmp_tgt_ = &cloud_tgt;
OptimizationFunctor functor (static_cast (cloud_src.points.size ()), this);
Eigen::NumericalDiff num_diff (functor);
//Eigen::LevenbergMarquardt, double> lm (num_diff);
Eigen::LevenbergMarquardt, MatScalar> lm (num_diff);
int info = lm.minimize (x);
// Compute the norm of the residuals
PCL_DEBUG ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation]");
PCL_DEBUG ("LM solver finished with exit code %i, having a residual norm of %g. \n", info, lm.fvec.norm ());
PCL_DEBUG ("Final solution: [%f", x[0]);
for (int i = 1; i < n_unknowns; ++i)
PCL_DEBUG (" %f", x[i]);
PCL_DEBUG ("]\n");
// Return the correct transformation
warp_point_->setParam (x);
transformation_matrix = warp_point_->getTransform ();
tmp_src_ = NULL;
tmp_tgt_ = NULL;
}
//
template void
pcl::registration::TransformationEstimationLM::estimateRigidTransformation (
const pcl::PointCloud &cloud_src,
const std::vector &indices_src,
const pcl::PointCloud &cloud_tgt,
Matrix4 &transformation_matrix) const
{
if (indices_src.size () != cloud_tgt.points.size ())
{
PCL_ERROR ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] Number or points in source (%lu) differs than target (%lu)!\n", indices_src.size (), cloud_tgt.points.size ());
return;
}
// is the source dataset
transformation_matrix.setIdentity ();
const int nr_correspondences = static_cast (cloud_tgt.points.size ());
std::vector indices_tgt;
indices_tgt.resize(nr_correspondences);
for (int i = 0; i < nr_correspondences; ++i)
indices_tgt[i] = i;
estimateRigidTransformation(cloud_src, indices_src, cloud_tgt, indices_tgt, transformation_matrix);
}
//
template inline void
pcl::registration::TransformationEstimationLM::estimateRigidTransformation (
const pcl::PointCloud &cloud_src,
const std::vector &indices_src,
const pcl::PointCloud &cloud_tgt,
const std::vector &indices_tgt,
Matrix4 &transformation_matrix) const
{
if (indices_src.size () != indices_tgt.size ())
{
PCL_ERROR ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] Number or points in source (%lu) differs than target (%lu)!\n", indices_src.size (), indices_tgt.size ());
return;
}
if (indices_src.size () < 4) // need at least 4 samples
{
PCL_ERROR ("[pcl::IterativeClosestPointNonLinear::estimateRigidTransformationLM] ");
PCL_ERROR ("Need at least 4 points to estimate a transform! Source and target have %lu points!",
indices_src.size ());
return;
}
int n_unknowns = warp_point_->getDimension (); // get dimension of unknown space
VectorX x (n_unknowns);
x.setConstant (n_unknowns, 0);
// Set temporary pointers
tmp_src_ = &cloud_src;
tmp_tgt_ = &cloud_tgt;
tmp_idx_src_ = &indices_src;
tmp_idx_tgt_ = &indices_tgt;
OptimizationFunctorWithIndices functor (static_cast (indices_src.size ()), this);
Eigen::NumericalDiff num_diff (functor);
//Eigen::LevenbergMarquardt > lm (num_diff);
Eigen::LevenbergMarquardt, MatScalar> lm (num_diff);
int info = lm.minimize (x);
// Compute the norm of the residuals
PCL_DEBUG ("[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] LM solver finished with exit code %i, having a residual norm of %g. \n", info, lm.fvec.norm ());
PCL_DEBUG ("Final solution: [%f", x[0]);
for (int i = 1; i < n_unknowns; ++i)
PCL_DEBUG (" %f", x[i]);
PCL_DEBUG ("]\n");
// Return the correct transformation
warp_point_->setParam (x);
transformation_matrix = warp_point_->getTransform ();
tmp_src_ = NULL;
tmp_tgt_ = NULL;
tmp_idx_src_ = tmp_idx_tgt_ = NULL;
}
//
template inline void
pcl::registration::TransformationEstimationLM::estimateRigidTransformation (
const pcl::PointCloud &cloud_src,
const pcl::PointCloud &cloud_tgt,
const pcl::Correspondences &correspondences,
Matrix4 &transformation_matrix) const
{
const int nr_correspondences = static_cast (correspondences.size ());
std::vector indices_src (nr_correspondences);
std::vector indices_tgt (nr_correspondences);
for (int i = 0; i < nr_correspondences; ++i)
{
indices_src[i] = correspondences[i].index_query;
indices_tgt[i] = correspondences[i].index_match;
}
estimateRigidTransformation (cloud_src, indices_src, cloud_tgt, indices_tgt, transformation_matrix);
}
//
template int
pcl::registration::TransformationEstimationLM::OptimizationFunctor::operator () (
const VectorX &x, VectorX &fvec) const
{
const PointCloud & src_points = *estimator_->tmp_src_;
const PointCloud & tgt_points = *estimator_->tmp_tgt_;
// Initialize the warp function with the given parameters
estimator_->warp_point_->setParam (x);
// Transform each source point and compute its distance to the corresponding target point
for (int i = 0; i < values (); ++i)
{
const PointSource & p_src = src_points.points[i];
const PointTarget & p_tgt = tgt_points.points[i];
// Transform the source point based on the current warp parameters
Vector4 p_src_warped;
estimator_->warp_point_->warpPoint (p_src, p_src_warped);
// Estimate the distance (cost function)
fvec[i] = estimator_->computeDistance (p_src_warped, p_tgt);
}
return (0);
}
//
template int
pcl::registration::TransformationEstimationLM::OptimizationFunctorWithIndices::operator() (
const VectorX &x, VectorX &fvec) const
{
const PointCloud & src_points = *estimator_->tmp_src_;
const PointCloud & tgt_points = *estimator_->tmp_tgt_;
const std::vector & src_indices = *estimator_->tmp_idx_src_;
const std::vector & tgt_indices = *estimator_->tmp_idx_tgt_;
// Initialize the warp function with the given parameters
estimator_->warp_point_->setParam (x);
// Transform each source point and compute its distance to the corresponding target point
for (int i = 0; i < values (); ++i)
{
const PointSource & p_src = src_points.points[src_indices[i]];
const PointTarget & p_tgt = tgt_points.points[tgt_indices[i]];
// Transform the source point based on the current warp parameters
Vector4 p_src_warped;
estimator_->warp_point_->warpPoint (p_src, p_src_warped);
// Estimate the distance (cost function)
fvec[i] = estimator_->computeDistance (p_src_warped, p_tgt);
}
return (0);
}
//#define PCL_INSTANTIATE_TransformationEstimationLM(T,U) template class PCL_EXPORTS pcl::registration::TransformationEstimationLM;
#endif /* PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_LM_HPP_ */
二、使用
/*配准*/
pcl::IterativeClosestPointNonLinear reg;//icpnl对象
// 设置 两次变换矩阵之间的停止差值 (默认 2)
reg.setTransformationEpsilon(1e-6);
//将两个对应关系之间的(src<->tgt)最大距离设置为10厘米
// 设置最大的对应关系距离为 多少 单位m， （对应距离大于此距离将被忽略）
reg.setMaxCorrespondenceDistance(15);
//设置点表示 继承与 Registration 类的 setPointRepresentation 的方法 功能 提供指向 PointRepresentation 的 boost 共享指针，以便在比较点时使用。
reg.setPointRepresentation(boost::make_shared(point_representation));
reg.setInputCloud(points_with_normals_src);
reg.setInputTarget(points_with_normals_tgt);
reg.setMaximumIterations(2);//设置最大迭代次数 因为以手动方式设置的很小
