238 lines
11 KiB
C
238 lines
11 KiB
C
|
/*
|
||
|
|
* Software License Agreement (BSD License)
|
||
|
|
*
|
||
|
|
* Point Cloud Library (PCL) - www.pointclouds.org
|
||
|
|
* Copyright (c) 2009, 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.
|
||
|
|
*
|
||
|
|
* $Id$
|
||
|
|
*
|
||
|
|
*/
|
||
|
|
|
||
|
|
#pragma once
|
||
|
|
|
||
|
|
#include <pcl/memory.h>
|
||
|
|
#include <pcl/pcl_macros.h>
|
||
|
|
#include <pcl/features/feature.h>
|
||
|
|
|
||
|
|
namespace pcl
|
||
|
|
{
|
||
|
|
/** \brief Transform a list of 2D matrices into a point cloud containing the values in a vector (Histogram<N>).
|
||
|
|
* Can be used to transform the 2D histograms obtained in \ref RSDEstimation into a point cloud.
|
||
|
|
* @note The template parameter N should be (greater or) equal to the product of the number of rows and columns.
|
||
|
|
* \param[in] histograms2D the list of neighborhood 2D histograms
|
||
|
|
* \param[out] histogramsPC the dataset containing the linearized matrices
|
||
|
|
* \ingroup features
|
||
|
|
*/
|
||
|
|
template <int N> void
|
||
|
|
getFeaturePointCloud (const std::vector<Eigen::MatrixXf, Eigen::aligned_allocator<Eigen::MatrixXf> > &histograms2D, PointCloud<Histogram<N> > &histogramsPC)
|
||
|
|
{
|
||
|
|
histogramsPC.resize (histograms2D.size ());
|
||
|
|
histogramsPC.width = histograms2D.size ();
|
||
|
|
histogramsPC.height = 1;
|
||
|
|
histogramsPC.is_dense = true;
|
||
|
|
|
||
|
|
const int rows = histograms2D.at(0).rows();
|
||
|
|
const int cols = histograms2D.at(0).cols();
|
||
|
|
|
||
|
|
typename PointCloud<Histogram<N> >::VectorType::iterator it = histogramsPC.begin ();
|
||
|
|
for (const Eigen::MatrixXf& h : histograms2D)
|
||
|
|
{
|
||
|
|
Eigen::Map<Eigen::MatrixXf> histogram (&(it->histogram[0]), rows, cols);
|
||
|
|
histogram = h;
|
||
|
|
++it;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals
|
||
|
|
* \param[in] surface the dataset containing the XYZ points
|
||
|
|
* \param[in] normals the dataset containing the surface normals at each point in the dataset
|
||
|
|
* \param[in] indices the neighborhood point indices in the dataset (first point is used as the reference)
|
||
|
|
* \param[in] max_dist the upper bound for the considered distance interval
|
||
|
|
* \param[in] nr_subdiv the number of subdivisions for the considered distance interval
|
||
|
|
* \param[in] plane_radius maximum radius, above which everything can be considered planar
|
||
|
|
* \param[in] radii the output point of a type that should have r_min and r_max fields
|
||
|
|
* \param[in] compute_histogram if not false, the full neighborhood histogram is provided, usable as a point signature
|
||
|
|
* \ingroup features
|
||
|
|
*/
|
||
|
|
template <typename PointInT, typename PointNT, typename PointOutT> Eigen::MatrixXf
|
||
|
|
computeRSD (const pcl::PointCloud<PointInT> &surface, const pcl::PointCloud<PointNT> &normals,
|
||
|
|
const pcl::Indices &indices, double max_dist,
|
||
|
|
int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram = false);
|
||
|
|
|
||
|
|
/** \brief Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals
|
||
|
|
* \param[in] normals the dataset containing the surface normals at each point in the dataset
|
||
|
|
* \param[in] indices the neighborhood point indices in the dataset (first point is used as the reference)
|
||
|
|
* \param[in] sqr_dists the squared distances from the first to all points in the neighborhood
|
||
|
|
* \param[in] max_dist the upper bound for the considered distance interval
|
||
|
|
* \param[in] nr_subdiv the number of subdivisions for the considered distance interval
|
||
|
|
* \param[in] plane_radius maximum radius, above which everything can be considered planar
|
||
|
|
* \param[in] radii the output point of a type that should have r_min and r_max fields
|
||
|
|
* \param[in] compute_histogram if not false, the full neighborhood histogram is provided, usable as a point signature
|
||
|
|
* \ingroup features
|
||
|
|
*/
|
||
|
|
template <typename PointNT, typename PointOutT> Eigen::MatrixXf
|
||
|
|
computeRSD (const pcl::PointCloud<PointNT> &normals,
|
||
|
|
const pcl::Indices &indices, const std::vector<float> &sqr_dists, double max_dist,
|
||
|
|
int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram = false);
|
||
|
|
|
||
|
|
/** \brief @b RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves)
|
||
|
|
* for a given point cloud dataset containing points and normals.
|
||
|
|
*
|
||
|
|
* @note If you use this code in any academic work, please cite:
|
||
|
|
*
|
||
|
|
* <ul>
|
||
|
|
* <li> Z.C. Marton , D. Pangercic , N. Blodow , J. Kleinehellefort, M. Beetz
|
||
|
|
* General 3D Modelling of Novel Objects from a Single View
|
||
|
|
* In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
|
||
|
|
* Taipei, Taiwan, October 18-22, 2010
|
||
|
|
* </li>
|
||
|
|
* <li> Z.C. Marton, D. Pangercic, N. Blodow, Michael Beetz.
|
||
|
|
* Combined 2D-3D Categorization and Classification for Multimodal Perception Systems.
|
||
|
|
* In The International Journal of Robotics Research, Sage Publications
|
||
|
|
* pages 1378--1402, Volume 30, Number 11, September 2011.
|
||
|
|
* </li>
|
||
|
|
* </ul>
|
||
|
|
*
|
||
|
|
* @note The code is stateful as we do not expect this class to be multicore parallelized.
|
||
|
|
* \author Zoltan-Csaba Marton
|
||
|
|
* \ingroup features
|
||
|
|
*/
|
||
|
|
template <typename PointInT, typename PointNT, typename PointOutT>
|
||
|
|
class RSDEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
using Feature<PointInT, PointOutT>::feature_name_;
|
||
|
|
using Feature<PointInT, PointOutT>::getClassName;
|
||
|
|
using Feature<PointInT, PointOutT>::indices_;
|
||
|
|
using Feature<PointInT, PointOutT>::search_radius_;
|
||
|
|
using Feature<PointInT, PointOutT>::search_parameter_;
|
||
|
|
using Feature<PointInT, PointOutT>::surface_;
|
||
|
|
using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
|
||
|
|
|
||
|
|
using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
|
||
|
|
using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
|
||
|
|
|
||
|
|
using Ptr = shared_ptr<RSDEstimation<PointInT, PointNT, PointOutT> >;
|
||
|
|
using ConstPtr = shared_ptr<const RSDEstimation<PointInT, PointNT, PointOutT> >;
|
||
|
|
|
||
|
|
|
||
|
|
/** \brief Empty constructor. */
|
||
|
|
RSDEstimation () : nr_subdiv_ (5), plane_radius_ (0.2), save_histograms_ (false)
|
||
|
|
{
|
||
|
|
feature_name_ = "RadiusSurfaceDescriptor";
|
||
|
|
};
|
||
|
|
|
||
|
|
/** \brief Set the number of subdivisions for the considered distance interval.
|
||
|
|
* \param[in] nr_subdiv the number of subdivisions
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setNrSubdivisions (int nr_subdiv) { nr_subdiv_ = nr_subdiv; }
|
||
|
|
|
||
|
|
/** \brief Get the number of subdivisions for the considered distance interval.
|
||
|
|
* \return the number of subdivisions
|
||
|
|
*/
|
||
|
|
inline int
|
||
|
|
getNrSubdivisions () const { return (nr_subdiv_); }
|
||
|
|
|
||
|
|
/** \brief Set the maximum radius, above which everything can be considered planar.
|
||
|
|
* \note the order of magnitude should be around 10-20 times the search radius (0.2 works well for typical datasets).
|
||
|
|
* \note on accurate 3D data (e.g. openni sernsors) a search radius as low as 0.01 still gives good results.
|
||
|
|
* \param[in] plane_radius the new plane radius
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setPlaneRadius (double plane_radius) { plane_radius_ = plane_radius; }
|
||
|
|
|
||
|
|
/** \brief Get the maximum radius, above which everything can be considered planar.
|
||
|
|
* \return the plane_radius used
|
||
|
|
*/
|
||
|
|
inline double
|
||
|
|
getPlaneRadius () const { return (plane_radius_); }
|
||
|
|
|
||
|
|
/** \brief Disables the setting of the number of k nearest neighbors to use for the feature estimation. */
|
||
|
|
inline void
|
||
|
|
setKSearch (int)
|
||
|
|
{
|
||
|
|
PCL_ERROR ("[pcl::%s::setKSearch] RSD does not work with k nearest neighbor search. Use setRadiusSearch() instead!\n", getClassName ().c_str ());
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Set whether the full distance-angle histograms should be saved.
|
||
|
|
* @note Obtain the list of histograms by getHistograms ()
|
||
|
|
* \param[in] save set to true if histograms should be saved
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setSaveHistograms (bool save) { save_histograms_ = save; }
|
||
|
|
|
||
|
|
/** \brief Returns whether the full distance-angle histograms are being saved.
|
||
|
|
* \return true if the histograms are being be saved
|
||
|
|
*/
|
||
|
|
inline bool
|
||
|
|
getSaveHistograms () const { return (save_histograms_); }
|
||
|
|
|
||
|
|
/** \brief Returns a pointer to the list of full distance-angle histograms for all points.
|
||
|
|
* \return the histogram being saved when computing RSD
|
||
|
|
*/
|
||
|
|
inline shared_ptr<std::vector<Eigen::MatrixXf, Eigen::aligned_allocator<Eigen::MatrixXf> > >
|
||
|
|
getHistograms () const { return (histograms_); }
|
||
|
|
|
||
|
|
protected:
|
||
|
|
|
||
|
|
/** \brief Estimate the estimates the Radius-based Surface Descriptor (RSD) at a set of points given by
|
||
|
|
* <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in
|
||
|
|
* setSearchMethod ()
|
||
|
|
* \param output the resultant point cloud model dataset that contains the RSD feature estimates (r_min and r_max values)
|
||
|
|
*/
|
||
|
|
void
|
||
|
|
computeFeature (PointCloudOut &output) override;
|
||
|
|
|
||
|
|
/** \brief The list of full distance-angle histograms for all points. */
|
||
|
|
shared_ptr<std::vector<Eigen::MatrixXf, Eigen::aligned_allocator<Eigen::MatrixXf> > > histograms_;
|
||
|
|
|
||
|
|
private:
|
||
|
|
/** \brief The number of subdivisions for the considered distance interval. */
|
||
|
|
int nr_subdiv_;
|
||
|
|
|
||
|
|
/** \brief The maximum radius, above which everything can be considered planar. */
|
||
|
|
double plane_radius_;
|
||
|
|
|
||
|
|
/** \brief Signals whether the full distance-angle histograms are being saved. */
|
||
|
|
bool save_histograms_;
|
||
|
|
|
||
|
|
public:
|
||
|
|
PCL_MAKE_ALIGNED_OPERATOR_NEW
|
||
|
|
};
|
||
|
|
}
|
||
|
|
|
||
|
|
#ifdef PCL_NO_PRECOMPILE
|
||
|
|
#include <pcl/features/impl/rsd.hpp>
|
||
|
|
#endif
|