shot_lrf.hpp
/*
* 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 <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 ();
std::vector<int> 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 (size_t i_idx = 0; i_idx < n_indices.size (); ++i_idx)
{
Eigen::Vector4f pt = surface_->points[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 (!pcl_isfinite (e1c) || !pcl_isfinite (e2c) || !pcl_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 (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_
pcl::geometry::distance
float distance(const PointT &p1, const PointT &p2)
Definition: geometry.h:60
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points. ...
Definition: projection_matrix.h:53
pcl::PointCloud::is_dense
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values).
Definition: point_cloud.h:418
pcl::SHOTLocalReferenceFrameEstimation::computeFeature
virtual void computeFeature(PointCloudOut &output)
Feature estimation method.
Definition: shot_lrf.hpp:169
pcl::SHOTLocalReferenceFrameEstimation::getLocalRF
float getLocalRF(const int &index, Eigen::Matrix3f &rf)
Computes disambiguated local RF for a point index.
Definition: shot_lrf.hpp:49