223 lines
10 KiB
C++
223 lines
10 KiB
C++
/*
|
|
* 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.
|
|
*
|
|
* $Id$
|
|
*
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include <pcl/point_types.h>
|
|
#include <pcl/features/feature.h>
|
|
#include <map>
|
|
#include <queue> // for std::queue
|
|
|
|
namespace pcl
|
|
{
|
|
/** \brief PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset
|
|
* containing points and normals.
|
|
*
|
|
* A commonly used type for PointOutT is pcl::PFHSignature125.
|
|
*
|
|
* \note If you use this code in any academic work, please cite:
|
|
*
|
|
* - R.B. Rusu, N. Blodow, Z.C. Marton, M. Beetz.
|
|
* Aligning Point Cloud Views using Persistent Feature Histograms.
|
|
* In Proceedings of the 21st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
|
|
* Nice, France, September 22-26 2008.
|
|
* - R.B. Rusu, Z.C. Marton, N. Blodow, M. Beetz.
|
|
* Learning Informative Point Classes for the Acquisition of Object Model Maps.
|
|
* In Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision (ICARCV),
|
|
* Hanoi, Vietnam, December 17-20 2008.
|
|
*
|
|
* \attention
|
|
* The convention for PFH features is:
|
|
* - if a query point's nearest neighbors cannot be estimated, the PFH feature will be set to NaN
|
|
* (not a number)
|
|
* - it is impossible to estimate a PFH descriptor for a point that
|
|
* doesn't have finite 3D coordinates. Therefore, any point that contains
|
|
* NaN data on x, y, or z, will have its PFH feature property set to NaN.
|
|
*
|
|
* \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
|
|
* \ref FPFHEstimationOMP for examples on parallel implementations of the FPFH (Fast Point Feature Histogram).
|
|
*
|
|
* \author Radu B. Rusu
|
|
* \ingroup features
|
|
*/
|
|
template <typename PointInT, typename PointNT, typename PointOutT = pcl::PFHSignature125>
|
|
class PFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
|
|
{
|
|
public:
|
|
using Ptr = shared_ptr<PFHEstimation<PointInT, PointNT, PointOutT> >;
|
|
using ConstPtr = shared_ptr<const PFHEstimation<PointInT, PointNT, PointOutT> >;
|
|
using Feature<PointInT, PointOutT>::feature_name_;
|
|
using Feature<PointInT, PointOutT>::getClassName;
|
|
using Feature<PointInT, PointOutT>::indices_;
|
|
using Feature<PointInT, PointOutT>::k_;
|
|
using Feature<PointInT, PointOutT>::search_parameter_;
|
|
using Feature<PointInT, PointOutT>::surface_;
|
|
using Feature<PointInT, PointOutT>::input_;
|
|
using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
|
|
|
|
using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
|
|
using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
|
|
|
|
/** \brief Empty constructor.
|
|
* Sets \a use_cache_ to false, \a nr_subdiv_ to 5, and the internal maximum cache size to 1GB.
|
|
*/
|
|
PFHEstimation () :
|
|
nr_subdiv_ (5),
|
|
d_pi_ (1.0f / (2.0f * static_cast<float> (M_PI))),
|
|
key_list_ (),
|
|
// Default 1GB memory size. Need to set it to something more conservative.
|
|
max_cache_size_ ((1ul*1024ul*1024ul*1024ul) / sizeof (std::pair<std::pair<int, int>, Eigen::Vector4f>)),
|
|
use_cache_ (false)
|
|
{
|
|
feature_name_ = "PFHEstimation";
|
|
};
|
|
|
|
/** \brief Set the maximum internal cache size. Defaults to 2GB worth of entries.
|
|
* \param[in] cache_size maximum cache size
|
|
*/
|
|
inline void
|
|
setMaximumCacheSize (unsigned int cache_size)
|
|
{
|
|
max_cache_size_ = cache_size;
|
|
}
|
|
|
|
/** \brief Get the maximum internal cache size. */
|
|
inline unsigned int
|
|
getMaximumCacheSize ()
|
|
{
|
|
return (max_cache_size_);
|
|
}
|
|
|
|
/** \brief Set whether to use an internal cache mechanism for removing redundant calculations or not.
|
|
*
|
|
* \note Depending on how the point cloud is ordered and how the nearest
|
|
* neighbors are estimated, using a cache could have a positive or a
|
|
* negative influence. Please test with and without a cache on your
|
|
* data, and choose whatever works best!
|
|
*
|
|
* See \ref setMaximumCacheSize for setting the maximum cache size
|
|
*
|
|
* \param[in] use_cache set to true to use the internal cache, false otherwise
|
|
*/
|
|
inline void
|
|
setUseInternalCache (bool use_cache)
|
|
{
|
|
use_cache_ = use_cache;
|
|
}
|
|
|
|
/** \brief Get whether the internal cache is used or not for computing the PFH features. */
|
|
inline bool
|
|
getUseInternalCache ()
|
|
{
|
|
return (use_cache_);
|
|
}
|
|
|
|
/** \brief Compute the 4-tuple representation containing the three angles and one distance between two points
|
|
* represented by Cartesian coordinates and normals.
|
|
* \note For explanations about the features, please see the literature mentioned above (the order of the
|
|
* features might be different).
|
|
* \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
|
|
* \param[in] normals the dataset containing the surface normals (assuming normalized vectors) at each point in cloud
|
|
* \param[in] p_idx the index of the first point (source)
|
|
* \param[in] q_idx the index of the second point (target)
|
|
* \param[out] f1 the first angular feature (angle between the projection of nq_idx and u)
|
|
* \param[out] f2 the second angular feature (angle between nq_idx and v)
|
|
* \param[out] f3 the third angular feature (angle between np_idx and |p_idx - q_idx|)
|
|
* \param[out] f4 the distance feature (p_idx - q_idx)
|
|
* \note For efficiency reasons, we assume that the point data passed to the method is finite.
|
|
*/
|
|
bool
|
|
computePairFeatures (const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
|
|
int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4);
|
|
|
|
/** \brief Estimate the PFH (Point Feature Histograms) individual signatures of the three angular (f1, f2, f3)
|
|
* features for a given point based on its spatial neighborhood of 3D points with normals
|
|
* \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
|
|
* \param[in] normals the dataset containing the surface normals at each point in \a cloud
|
|
* \param[in] indices the k-neighborhood point indices in the dataset
|
|
* \param[in] nr_split the number of subdivisions for each angular feature interval
|
|
* \param[out] pfh_histogram the resultant (combinatorial) PFH histogram representing the feature at the query point
|
|
*/
|
|
void
|
|
computePointPFHSignature (const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
|
|
const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfh_histogram);
|
|
|
|
protected:
|
|
/** \brief Estimate the Point Feature Histograms (PFH) descriptors at a set of points given by
|
|
* <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in
|
|
* setSearchMethod ()
|
|
* \param[out] output the resultant point cloud model dataset that contains the PFH feature estimates
|
|
*/
|
|
void
|
|
computeFeature (PointCloudOut &output) override;
|
|
|
|
/** \brief The number of subdivisions for each angular feature interval. */
|
|
int nr_subdiv_;
|
|
|
|
/** \brief Placeholder for a point's PFH signature. */
|
|
Eigen::VectorXf pfh_histogram_;
|
|
|
|
/** \brief Placeholder for a PFH 4-tuple. */
|
|
Eigen::Vector4f pfh_tuple_;
|
|
|
|
/** \brief Placeholder for a histogram index. */
|
|
int f_index_[3];
|
|
|
|
/** \brief Float constant = 1.0 / (2.0 * M_PI) */
|
|
float d_pi_;
|
|
|
|
/** \brief Internal hashmap, used to optimize efficiency of redundant computations. */
|
|
std::map<std::pair<int, int>, Eigen::Vector4f, std::less<>, Eigen::aligned_allocator<std::pair<const std::pair<int, int>, Eigen::Vector4f> > > feature_map_;
|
|
|
|
/** \brief Queue of pairs saved, used to constrain memory usage. */
|
|
std::queue<std::pair<int, int> > key_list_;
|
|
|
|
/** \brief Maximum size of internal cache memory. */
|
|
unsigned int max_cache_size_;
|
|
|
|
/** \brief Set to true to use the internal cache for removing redundant computations. */
|
|
bool use_cache_;
|
|
};
|
|
}
|
|
|
|
#ifdef PCL_NO_PRECOMPILE
|
|
#include <pcl/features/impl/pfh.hpp>
|
|
#endif
|