288 lines
11 KiB
C
288 lines
11 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/features/feature.h>
|
||
|
|
#include <pcl/features/vfh.h>
|
||
|
|
#include <pcl/search/search.h> // for Search
|
||
|
|
|
||
|
|
namespace pcl
|
||
|
|
{
|
||
|
|
/** \brief CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given
|
||
|
|
* point cloud dataset containing XYZ data and normals, as presented in:
|
||
|
|
* - CAD-Model Recognition and 6 DOF Pose Estimation
|
||
|
|
* A. Aldoma, N. Blodow, D. Gossow, S. Gedikli, R.B. Rusu, M. Vincze and G. Bradski
|
||
|
|
* ICCV 2011, 3D Representation and Recognition (3dRR11) workshop
|
||
|
|
* Barcelona, Spain, (2011)
|
||
|
|
*
|
||
|
|
* The suggested PointOutT is pcl::VFHSignature308.
|
||
|
|
*
|
||
|
|
* \author Aitor Aldoma
|
||
|
|
* \ingroup features
|
||
|
|
*/
|
||
|
|
template<typename PointInT, typename PointNT, typename PointOutT = pcl::VFHSignature308>
|
||
|
|
class CVFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
using Ptr = shared_ptr<CVFHEstimation<PointInT, PointNT, PointOutT> >;
|
||
|
|
using ConstPtr = shared_ptr<const CVFHEstimation<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_radius_;
|
||
|
|
using Feature<PointInT, PointOutT>::surface_;
|
||
|
|
using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
|
||
|
|
|
||
|
|
using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
|
||
|
|
using KdTreePtr = typename pcl::search::Search<PointNormal>::Ptr;
|
||
|
|
using VFHEstimator = pcl::VFHEstimation<PointInT, PointNT, pcl::VFHSignature308>;
|
||
|
|
|
||
|
|
/** \brief Empty constructor. */
|
||
|
|
CVFHEstimation () :
|
||
|
|
vpx_ (0), vpy_ (0), vpz_ (0),
|
||
|
|
leaf_size_ (0.005f),
|
||
|
|
normalize_bins_ (false),
|
||
|
|
curv_threshold_ (0.03f),
|
||
|
|
cluster_tolerance_ (leaf_size_ * 3),
|
||
|
|
eps_angle_threshold_ (0.125f),
|
||
|
|
min_points_ (50),
|
||
|
|
radius_normals_ (leaf_size_ * 3)
|
||
|
|
{
|
||
|
|
search_radius_ = 0;
|
||
|
|
k_ = 1;
|
||
|
|
feature_name_ = "CVFHEstimation";
|
||
|
|
}
|
||
|
|
;
|
||
|
|
|
||
|
|
/** \brief Removes normals with high curvature caused by real edges or noisy data
|
||
|
|
* \param[in] cloud pointcloud to be filtered
|
||
|
|
* \param[in] indices_to_use the indices to use
|
||
|
|
* \param[out] indices_out the indices of the points with higher curvature than threshold
|
||
|
|
* \param[out] indices_in the indices of the remaining points after filtering
|
||
|
|
* \param[in] threshold threshold value for curvature
|
||
|
|
*/
|
||
|
|
void
|
||
|
|
filterNormalsWithHighCurvature (const pcl::PointCloud<PointNT> & cloud, pcl::Indices & indices_to_use, pcl::Indices &indices_out,
|
||
|
|
pcl::Indices &indices_in, float threshold);
|
||
|
|
|
||
|
|
/** \brief Set the viewpoint.
|
||
|
|
* \param[in] vpx the X coordinate of the viewpoint
|
||
|
|
* \param[in] vpy the Y coordinate of the viewpoint
|
||
|
|
* \param[in] vpz the Z coordinate of the viewpoint
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setViewPoint (float vpx, float vpy, float vpz)
|
||
|
|
{
|
||
|
|
vpx_ = vpx;
|
||
|
|
vpy_ = vpy;
|
||
|
|
vpz_ = vpz;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Set the radius used to compute normals
|
||
|
|
* \param[in] radius_normals the radius
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setRadiusNormals (float radius_normals)
|
||
|
|
{
|
||
|
|
radius_normals_ = radius_normals;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Get the viewpoint.
|
||
|
|
* \param[out] vpx the X coordinate of the viewpoint
|
||
|
|
* \param[out] vpy the Y coordinate of the viewpoint
|
||
|
|
* \param[out] vpz the Z coordinate of the viewpoint
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
getViewPoint (float &vpx, float &vpy, float &vpz)
|
||
|
|
{
|
||
|
|
vpx = vpx_;
|
||
|
|
vpy = vpy_;
|
||
|
|
vpz = vpz_;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Get the centroids used to compute different CVFH descriptors
|
||
|
|
* \param[out] centroids vector to hold the centroids
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
getCentroidClusters (std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > & centroids)
|
||
|
|
{
|
||
|
|
centroids.insert (centroids.cend (), centroids_dominant_orientations_.cbegin (),
|
||
|
|
centroids_dominant_orientations_.cend ());
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Get the normal centroids used to compute different CVFH descriptors
|
||
|
|
* \param[out] centroids vector to hold the normal centroids
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
getCentroidNormalClusters (std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > & centroids)
|
||
|
|
{
|
||
|
|
for (const auto& normal: dominant_normals_)
|
||
|
|
centroids.push_back (normal);
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Sets max. Euclidean distance between points to be added to the cluster
|
||
|
|
* \param[in] d the maximum Euclidean distance
|
||
|
|
*/
|
||
|
|
|
||
|
|
inline void
|
||
|
|
setClusterTolerance (float d)
|
||
|
|
{
|
||
|
|
cluster_tolerance_ = d;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Sets max. deviation of the normals between two points so they can be clustered together
|
||
|
|
* \param[in] d the maximum deviation
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setEPSAngleThreshold (float d)
|
||
|
|
{
|
||
|
|
eps_angle_threshold_ = d;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Sets curvature threshold for removing normals
|
||
|
|
* \param[in] d the curvature threshold
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setCurvatureThreshold (float d)
|
||
|
|
{
|
||
|
|
curv_threshold_ = d;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Set minimum amount of points for a cluster to be considered
|
||
|
|
* \param[in] min the minimum amount of points to be set
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setMinPoints (std::size_t min)
|
||
|
|
{
|
||
|
|
min_points_ = min;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Sets whether if the CVFH signatures should be normalized or not
|
||
|
|
* \param[in] normalize true if normalization is required, false otherwise
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setNormalizeBins (bool normalize)
|
||
|
|
{
|
||
|
|
normalize_bins_ = normalize;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Overloaded computed method from pcl::Feature.
|
||
|
|
* \param[out] output the resultant point cloud model dataset containing the estimated features
|
||
|
|
*/
|
||
|
|
void
|
||
|
|
compute (PointCloudOut &output);
|
||
|
|
|
||
|
|
private:
|
||
|
|
/** \brief Values describing the viewpoint ("pinhole" camera model assumed).
|
||
|
|
* By default, the viewpoint is set to 0,0,0.
|
||
|
|
*/
|
||
|
|
float vpx_, vpy_, vpz_;
|
||
|
|
|
||
|
|
/** \brief Size of the voxels after voxel gridding. IMPORTANT: Must match the voxel
|
||
|
|
* size of the training data or the normalize_bins_ flag must be set to true.
|
||
|
|
*/
|
||
|
|
float leaf_size_;
|
||
|
|
|
||
|
|
/** \brief Whether to normalize the signatures or not. Default: false. */
|
||
|
|
bool normalize_bins_;
|
||
|
|
|
||
|
|
/** \brief Curvature threshold for removing normals. */
|
||
|
|
float curv_threshold_;
|
||
|
|
|
||
|
|
/** \brief allowed Euclidean distance between points to be added to the cluster. */
|
||
|
|
float cluster_tolerance_;
|
||
|
|
|
||
|
|
/** \brief deviation of the normals between two points so they can be clustered together. */
|
||
|
|
float eps_angle_threshold_;
|
||
|
|
|
||
|
|
/** \brief Minimum amount of points in a clustered region to be considered stable for CVFH
|
||
|
|
* computation.
|
||
|
|
*/
|
||
|
|
std::size_t min_points_;
|
||
|
|
|
||
|
|
/** \brief Radius for the normals computation. */
|
||
|
|
float radius_normals_;
|
||
|
|
|
||
|
|
/** \brief Estimate the Clustered Viewpoint Feature Histograms (CVFH) descriptors at
|
||
|
|
* a set of points given by <setInputCloud (), setIndices ()> using the surface in
|
||
|
|
* setSearchSurface ()
|
||
|
|
*
|
||
|
|
* \param[out] output the resultant point cloud model dataset that contains the CVFH
|
||
|
|
* feature estimates
|
||
|
|
*/
|
||
|
|
void
|
||
|
|
computeFeature (PointCloudOut &output) override;
|
||
|
|
|
||
|
|
/** \brief Region growing method using Euclidean distances and neighbors normals to
|
||
|
|
* add points to a region.
|
||
|
|
* \param[in] cloud point cloud to split into regions
|
||
|
|
* \param[in] normals are the normals of cloud
|
||
|
|
* \param[in] tolerance is the allowed Euclidean distance between points to be added to
|
||
|
|
* the cluster
|
||
|
|
* \param[in] tree is the spatial search structure for nearest neighbour search
|
||
|
|
* \param[out] clusters vector of indices representing the clustered regions
|
||
|
|
* \param[in] eps_angle deviation of the normals between two points so they can be
|
||
|
|
* clustered together
|
||
|
|
* \param[in] min_pts_per_cluster minimum cluster size. (default: 1 point)
|
||
|
|
* \param[in] max_pts_per_cluster maximum cluster size. (default: all the points)
|
||
|
|
*/
|
||
|
|
void
|
||
|
|
extractEuclideanClustersSmooth (const pcl::PointCloud<pcl::PointNormal> &cloud,
|
||
|
|
const pcl::PointCloud<pcl::PointNormal> &normals, float tolerance,
|
||
|
|
const pcl::search::Search<pcl::PointNormal>::Ptr &tree,
|
||
|
|
std::vector<pcl::PointIndices> &clusters, double eps_angle,
|
||
|
|
unsigned int min_pts_per_cluster = 1,
|
||
|
|
unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ());
|
||
|
|
|
||
|
|
protected:
|
||
|
|
/** \brief Centroids that were used to compute different CVFH descriptors */
|
||
|
|
std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > centroids_dominant_orientations_;
|
||
|
|
/** \brief Normal centroids that were used to compute different CVFH descriptors */
|
||
|
|
std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > dominant_normals_;
|
||
|
|
};
|
||
|
|
}
|
||
|
|
|
||
|
|
#ifdef PCL_NO_PRECOMPILE
|
||
|
|
#include <pcl/features/impl/cvfh.hpp>
|
||
|
|
#endif
|