208 lines
7.2 KiB
C++
208 lines
7.2 KiB
C++
|
/*
|
||
|
|
* Software License Agreement (BSD License)
|
||
|
|
*
|
||
|
|
* Point Cloud Library (PCL) - www.pointclouds.org
|
||
|
|
* Copyright (c) 2012, 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_FEATURES_IMPL_SHOT_LRF_H_
|
||
|
|
#define PCL_FEATURES_IMPL_SHOT_LRF_H_
|
||
|
|
|
||
|
|
#include <Eigen/Eigenvalues> // for SelfAdjointEigenSolver
|
||
|
|
#include <utility>
|
||
|
|
#include <pcl/features/shot_lrf.h>
|
||
|
|
|
||
|
|
//////////////////////////////////////////////////////////////////////////////////////////////
|
||
|
|
// Compute a local Reference Frame for a 3D feature; the output is stored in the "rf" matrix
|
||
|
|
template<typename PointInT, typename PointOutT> float
|
||
|
|
pcl::SHOTLocalReferenceFrameEstimation<PointInT, PointOutT>::getLocalRF (const int& current_point_idx, Eigen::Matrix3f &rf)
|
||
|
|
{
|
||
|
|
const Eigen::Vector4f& central_point = (*input_)[current_point_idx].getVector4fMap ();
|
||
|
|
pcl::Indices n_indices;
|
||
|
|
std::vector<float> n_sqr_distances;
|
||
|
|
|
||
|
|
this->searchForNeighbors (current_point_idx, search_parameter_, n_indices, n_sqr_distances);
|
||
|
|
|
||
|
|
Eigen::Matrix<double, Eigen::Dynamic, 4> vij (n_indices.size (), 4);
|
||
|
|
|
||
|
|
Eigen::Matrix3d cov_m = Eigen::Matrix3d::Zero ();
|
||
|
|
|
||
|
|
double distance = 0.0;
|
||
|
|
double sum = 0.0;
|
||
|
|
|
||
|
|
int valid_nn_points = 0;
|
||
|
|
|
||
|
|
for (std::size_t i_idx = 0; i_idx < n_indices.size (); ++i_idx)
|
||
|
|
{
|
||
|
|
Eigen::Vector4f pt = (*surface_)[n_indices[i_idx]].getVector4fMap ();
|
||
|
|
if (pt.head<3> () == central_point.head<3> ())
|
||
|
|
continue;
|
||
|
|
|
||
|
|
// Difference between current point and origin
|
||
|
|
vij.row (valid_nn_points).matrix () = (pt - central_point).cast<double> ();
|
||
|
|
vij (valid_nn_points, 3) = 0;
|
||
|
|
|
||
|
|
distance = search_parameter_ - sqrt (n_sqr_distances[i_idx]);
|
||
|
|
|
||
|
|
// Multiply vij * vij'
|
||
|
|
cov_m += distance * (vij.row (valid_nn_points).head<3> ().transpose () * vij.row (valid_nn_points).head<3> ());
|
||
|
|
|
||
|
|
sum += distance;
|
||
|
|
valid_nn_points++;
|
||
|
|
}
|
||
|
|
|
||
|
|
if (valid_nn_points < 5)
|
||
|
|
{
|
||
|
|
//PCL_ERROR ("[pcl::%s::getLocalRF] Warning! Neighborhood has less than 5 vertexes. Aborting Local RF computation of feature point (%lf, %lf, %lf)\n", "SHOTLocalReferenceFrameEstimation", central_point[0], central_point[1], central_point[2]);
|
||
|
|
rf.setConstant (std::numeric_limits<float>::quiet_NaN ());
|
||
|
|
|
||
|
|
return (std::numeric_limits<float>::max ());
|
||
|
|
}
|
||
|
|
|
||
|
|
cov_m /= sum;
|
||
|
|
|
||
|
|
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> solver (cov_m);
|
||
|
|
|
||
|
|
const double& e1c = solver.eigenvalues ()[0];
|
||
|
|
const double& e2c = solver.eigenvalues ()[1];
|
||
|
|
const double& e3c = solver.eigenvalues ()[2];
|
||
|
|
|
||
|
|
if (!std::isfinite (e1c) || !std::isfinite (e2c) || !std::isfinite (e3c))
|
||
|
|
{
|
||
|
|
//PCL_ERROR ("[pcl::%s::getLocalRF] Warning! Eigenvectors are NaN. Aborting Local RF computation of feature point (%lf, %lf, %lf)\n", "SHOTLocalReferenceFrameEstimation", central_point[0], central_point[1], central_point[2]);
|
||
|
|
rf.setConstant (std::numeric_limits<float>::quiet_NaN ());
|
||
|
|
|
||
|
|
return (std::numeric_limits<float>::max ());
|
||
|
|
}
|
||
|
|
|
||
|
|
// Disambiguation
|
||
|
|
Eigen::Vector4d v1 = Eigen::Vector4d::Zero ();
|
||
|
|
Eigen::Vector4d v3 = Eigen::Vector4d::Zero ();
|
||
|
|
v1.head<3> ().matrix () = solver.eigenvectors ().col (2);
|
||
|
|
v3.head<3> ().matrix () = solver.eigenvectors ().col (0);
|
||
|
|
|
||
|
|
int plusNormal = 0, plusTangentDirection1=0;
|
||
|
|
for (int ne = 0; ne < valid_nn_points; ne++)
|
||
|
|
{
|
||
|
|
double dp = vij.row (ne).dot (v1);
|
||
|
|
if (dp >= 0)
|
||
|
|
plusTangentDirection1++;
|
||
|
|
|
||
|
|
dp = vij.row (ne).dot (v3);
|
||
|
|
if (dp >= 0)
|
||
|
|
plusNormal++;
|
||
|
|
}
|
||
|
|
|
||
|
|
//TANGENT
|
||
|
|
plusTangentDirection1 = 2*plusTangentDirection1 - valid_nn_points;
|
||
|
|
if (plusTangentDirection1 == 0)
|
||
|
|
{
|
||
|
|
int points = 5; //std::min(valid_nn_points*2/2+1, 11);
|
||
|
|
int medianIndex = valid_nn_points/2;
|
||
|
|
|
||
|
|
for (int i = -points/2; i <= points/2; i++)
|
||
|
|
if ( vij.row (medianIndex - i).dot (v1) > 0)
|
||
|
|
plusTangentDirection1 ++;
|
||
|
|
|
||
|
|
if (plusTangentDirection1 < points/2+1)
|
||
|
|
v1 *= - 1;
|
||
|
|
}
|
||
|
|
else if (plusTangentDirection1 < 0)
|
||
|
|
v1 *= - 1;
|
||
|
|
|
||
|
|
//Normal
|
||
|
|
plusNormal = 2*plusNormal - valid_nn_points;
|
||
|
|
if (plusNormal == 0)
|
||
|
|
{
|
||
|
|
int points = 5; //std::min(valid_nn_points*2/2+1, 11);
|
||
|
|
int medianIndex = valid_nn_points/2;
|
||
|
|
|
||
|
|
for (int i = -points/2; i <= points/2; i++)
|
||
|
|
if ( vij.row (medianIndex - i).dot (v3) > 0)
|
||
|
|
plusNormal ++;
|
||
|
|
|
||
|
|
if (plusNormal < points/2+1)
|
||
|
|
v3 *= - 1;
|
||
|
|
} else if (plusNormal < 0)
|
||
|
|
v3 *= - 1;
|
||
|
|
|
||
|
|
rf.row (0).matrix () = v1.head<3> ().cast<float> ();
|
||
|
|
rf.row (2).matrix () = v3.head<3> ().cast<float> ();
|
||
|
|
rf.row (1).matrix () = rf.row (2).cross (rf.row (0));
|
||
|
|
|
||
|
|
return (0.0f);
|
||
|
|
}
|
||
|
|
|
||
|
|
//////////////////////////////////////////////////////////////////////////////////////////////
|
||
|
|
template <typename PointInT, typename PointOutT> void
|
||
|
|
pcl::SHOTLocalReferenceFrameEstimation<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
|
||
|
|
{
|
||
|
|
//check whether used with search radius or search k-neighbors
|
||
|
|
if (this->getKSearch () != 0)
|
||
|
|
{
|
||
|
|
PCL_ERROR(
|
||
|
|
"[pcl::%s::computeFeature] Error! Search method set to k-neighborhood. Call setKSearch(0) and setRadiusSearch( radius ) to use this class.\n",
|
||
|
|
getClassName().c_str ());
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
tree_->setSortedResults (true);
|
||
|
|
|
||
|
|
for (std::size_t i = 0; i < indices_->size (); ++i)
|
||
|
|
{
|
||
|
|
// point result
|
||
|
|
Eigen::Matrix3f rf;
|
||
|
|
PointOutT& output_rf = output[i];
|
||
|
|
|
||
|
|
//output_rf.confidence = getLocalRF ((*indices_)[i], rf);
|
||
|
|
//if (output_rf.confidence == std::numeric_limits<float>::max ())
|
||
|
|
if (getLocalRF ((*indices_)[i], rf) == std::numeric_limits<float>::max ())
|
||
|
|
{
|
||
|
|
output.is_dense = false;
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int d = 0; d < 3; ++d)
|
||
|
|
{
|
||
|
|
output_rf.x_axis[d] = rf.row (0)[d];
|
||
|
|
output_rf.y_axis[d] = rf.row (1)[d];
|
||
|
|
output_rf.z_axis[d] = rf.row (2)[d];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
#define PCL_INSTANTIATE_SHOTLocalReferenceFrameEstimation(T,OutT) template class PCL_EXPORTS pcl::SHOTLocalReferenceFrameEstimation<T,OutT>;
|
||
|
|
|
||
|
|
#endif // PCL_FEATURES_IMPL_SHOT_LRF_H_
|
||
|
|
|