transformation_estimation_lm.hpp
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2011, Willow Garage, Inc.
* Copyright (c) 2012-, Open Perception, Inc.
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the copyright holder(s) nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* $Id$
*
*/
#ifndef PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_LM_HPP_
#define PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_LM_HPP_
#include <pcl/registration/warp_point_rigid.h>
#include <pcl/registration/warp_point_rigid_6d.h>
#include <pcl/registration/distances.h>
#include <unsupported/Eigen/NonLinearOptimization>
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename MatScalar>
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::TransformationEstimationLM ()
: tmp_src_ ()
, tmp_tgt_ ()
, tmp_idx_src_ ()
, tmp_idx_tgt_ ()
, warp_point_ (new WarpPointRigid6D<PointSource, PointTarget, MatScalar>)
{
};
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename MatScalar> void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::estimateRigidTransformation (
const pcl::PointCloud<PointSource> &cloud_src,
const pcl::PointCloud<PointTarget> &cloud_tgt,
Matrix4 &transformation_matrix) const
{
// <cloud_src,cloud_src> 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<int> (cloud_src.points.size ()), this);
Eigen::NumericalDiff<OptimizationFunctor> num_diff (functor);
//Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctor>, double> lm (num_diff);
Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctor>, 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 <typename PointSource, typename PointTarget, typename MatScalar> void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::estimateRigidTransformation (
const pcl::PointCloud<PointSource> &cloud_src,
const std::vector<int> &indices_src,
const pcl::PointCloud<PointTarget> &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;
}
// <cloud_src,cloud_src> is the source dataset
transformation_matrix.setIdentity ();
const int nr_correspondences = static_cast<const int> (cloud_tgt.points.size ());
std::vector<int> 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 <typename PointSource, typename PointTarget, typename MatScalar> inline void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::estimateRigidTransformation (
const pcl::PointCloud<PointSource> &cloud_src,
const std::vector<int> &indices_src,
const pcl::PointCloud<PointTarget> &cloud_tgt,
const std::vector<int> &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<int> (indices_src.size ()), this);
Eigen::NumericalDiff<OptimizationFunctorWithIndices> num_diff (functor);
//Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctorWithIndices> > lm (num_diff);
Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctorWithIndices>, 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 <typename PointSource, typename PointTarget, typename MatScalar> inline void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::estimateRigidTransformation (
const pcl::PointCloud<PointSource> &cloud_src,
const pcl::PointCloud<PointTarget> &cloud_tgt,
const pcl::Correspondences &correspondences,
Matrix4 &transformation_matrix) const
{
const int nr_correspondences = static_cast<const int> (correspondences.size ());
std::vector<int> indices_src (nr_correspondences);
std::vector<int> 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 <typename PointSource, typename PointTarget, typename MatScalar> int
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::OptimizationFunctor::operator () (
const VectorX &x, VectorX &fvec) const
{
const PointCloud<PointSource> & src_points = *estimator_->tmp_src_;
const PointCloud<PointTarget> & 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 <typename PointSource, typename PointTarget, typename MatScalar> int
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::OptimizationFunctorWithIndices::operator() (
const VectorX &x, VectorX &fvec) const
{
const PointCloud<PointSource> & src_points = *estimator_->tmp_src_;
const PointCloud<PointTarget> & tgt_points = *estimator_->tmp_tgt_;
const std::vector<int> & src_indices = *estimator_->tmp_idx_src_;
const std::vector<int> & 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<T,U>;
#endif /* PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_LM_HPP_ */
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
pcl::registration::TransformationEstimationLM::OptimizationFunctorWithIndices::operator()
int operator()(const VectorX &x, VectorX &fvec) const
Fill fvec from x.
Definition: transformation_estimation_lm.hpp:244
pcl::registration::TransformationEstimationLM::TransformationEstimationLM
TransformationEstimationLM()
Constructor.
Definition: transformation_estimation_lm.hpp:51
pcl::registration::TransformationEstimationLM::OptimizationFunctor::operator()
int operator()(const VectorX &x, VectorX &fvec) const
Fill fvec from x.
Definition: transformation_estimation_lm.hpp:217
pcl::Correspondences
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
Definition: correspondence.h:92
pcl::registration::TransformationEstimation::Matrix4
Eigen::Matrix< Scalar, 4, 4 > Matrix4
Definition: transformation_estimation.h:65
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, Scalar >::VectorX
Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > VectorX
Definition: transformation_estimation_lm.h:74
pcl::PointCloud< PointSource >
pcl::registration::WarpPointRigid6D
WarpPointRigid3D enables 6D (3D rotation + 3D translation) transformations for points.
Definition: warp_point_rigid_6d.h:59
pcl::registration::TransformationEstimationLM::estimateRigidTransformation
void estimateRigidTransformation(const pcl::PointCloud< PointSource > &cloud_src, const pcl::PointCloud< PointTarget > &cloud_tgt, Matrix4 &transformation_matrix) const
Estimate a rigid rotation transformation between a source and a target point cloud using LM...
Definition: transformation_estimation_lm.hpp:62
pcl::registration::TransformationEstimationLM::Vector4
Eigen::Matrix< MatScalar, 4, 1 > Vector4
Definition: transformation_estimation_lm.h:75