128 lines
5.4 KiB
C++
128 lines
5.4 KiB
C++
/*
|
|
* Software License Agreement (BSD License)
|
|
*
|
|
* Point Cloud Library (PCL) - www.pointclouds.org
|
|
* Copyright (c) 2009-2014, Willow Garage, Inc.
|
|
* Copyright (c) 2014-, 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.
|
|
*
|
|
*/
|
|
|
|
#ifndef PCL_FEATURES_IMPL_GRSD_H_
|
|
#define PCL_FEATURES_IMPL_GRSD_H_
|
|
|
|
#include <pcl/features/grsd.h>
|
|
#include <pcl/features/rsd.h> // for RSDEstimation
|
|
///////// STATIC /////////
|
|
template <typename PointInT, typename PointNT, typename PointOutT> int
|
|
pcl::GRSDEstimation<PointInT, PointNT, PointOutT>::getSimpleType (float min_radius, float max_radius,
|
|
double min_radius_plane,
|
|
double max_radius_noise,
|
|
double min_radius_cylinder,
|
|
double max_min_radius_diff)
|
|
{
|
|
if (min_radius > min_radius_plane)
|
|
return (1); // plane
|
|
if (max_radius > min_radius_cylinder)
|
|
return (2); // cylinder (rim)
|
|
if (min_radius < max_radius_noise)
|
|
return (0); // noise/corner
|
|
if (max_radius - min_radius < max_min_radius_diff)
|
|
return (3); // sphere/corner
|
|
return (4); // edge
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename PointInT, typename PointNT, typename PointOutT> void
|
|
pcl::GRSDEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
|
|
{
|
|
// Check if search_radius_ was set
|
|
if (width_ < 0)
|
|
{
|
|
PCL_ERROR ("[pcl::%s::computeFeature] A voxel cell width needs to be set!\n", getClassName ().c_str ());
|
|
output.width = output.height = 0;
|
|
output.clear ();
|
|
return;
|
|
}
|
|
|
|
// Create the voxel grid
|
|
PointCloudInPtr cloud_downsampled (new PointCloudIn());
|
|
pcl::VoxelGrid<PointInT> grid;
|
|
grid.setLeafSize (width_, width_, width_);
|
|
grid.setInputCloud (input_);
|
|
grid.setSaveLeafLayout (true); // TODO maybe avoid this using nearest neighbor search
|
|
grid.filter (*cloud_downsampled);
|
|
|
|
// Compute RSD
|
|
pcl::PointCloud<pcl::PrincipalRadiiRSD>::Ptr radii (new pcl::PointCloud<pcl::PrincipalRadiiRSD>());
|
|
pcl::RSDEstimation<PointInT, PointNT, pcl::PrincipalRadiiRSD> rsd;
|
|
rsd.setInputCloud (cloud_downsampled);
|
|
rsd.setSearchSurface (input_);
|
|
rsd.setInputNormals (normals_);
|
|
rsd.setRadiusSearch (std::max (search_radius_, std::sqrt (3.0) * width_ / 2));
|
|
rsd.compute (*radii);
|
|
|
|
// Save the type of each point
|
|
int NR_CLASS = 5; // TODO make this nicer
|
|
std::vector<int> types (radii->size ());
|
|
std::transform(radii->points.cbegin (), radii->points.cend (), types.begin (),
|
|
[](const auto& point) {
|
|
// GCC 5.4 can't find unqualified getSimpleType
|
|
return GRSDEstimation<PointInT, PointNT, PointOutT>::getSimpleType(point.r_min, point.r_max); });
|
|
|
|
// Get the transitions between surface types between neighbors of occupied cells
|
|
Eigen::MatrixXi transition_matrix = Eigen::MatrixXi::Zero (NR_CLASS + 1, NR_CLASS + 1);
|
|
for (std::size_t idx = 0; idx < cloud_downsampled->size (); ++idx)
|
|
{
|
|
const int source_type = types[idx];
|
|
std::vector<int> neighbors = grid.getNeighborCentroidIndices ((*cloud_downsampled)[idx], relative_coordinates_all_);
|
|
for (const int &neighbor : neighbors)
|
|
{
|
|
int neighbor_type = NR_CLASS;
|
|
if (neighbor != -1) // not empty
|
|
neighbor_type = types[neighbor];
|
|
transition_matrix (source_type, neighbor_type)++;
|
|
}
|
|
}
|
|
|
|
// Save feature values
|
|
output.resize (1);
|
|
output.height = output.width = 1;
|
|
int nrf = 0;
|
|
for (int i = 0; i < NR_CLASS + 1; i++)
|
|
for (int j = i; j < NR_CLASS + 1; j++)
|
|
output[0].histogram[nrf++] = transition_matrix (i, j) + transition_matrix (j, i);
|
|
}
|
|
|
|
#define PCL_INSTANTIATE_GRSDEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::GRSDEstimation<T,NT,OutT>;
|
|
|
|
#endif /* PCL_FEATURES_IMPL_GRSD_H_ */
|