500 lines
20 KiB
C
500 lines
20 KiB
C
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/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2010-2011, Willow Garage, Inc.
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* Copyright (c) 2012-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* $Id$
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*
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*/
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#pragma once
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#if defined __GNUC__
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# pragma GCC system_header
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#endif
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// PCL includes
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#include <pcl/memory.h>
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#include <pcl/pcl_base.h>
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#include <pcl/pcl_macros.h>
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#include <pcl/search/search.h>
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#include <functional>
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namespace pcl
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{
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/** \brief Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares
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* plane normal and surface curvature.
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* \param covariance_matrix the 3x3 covariance matrix
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* \param point a point lying on the least-squares plane (SSE aligned)
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* \param plane_parameters the resultant plane parameters as: a, b, c, d (ax + by + cz + d = 0)
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* \param curvature the estimated surface curvature as a measure of
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* \f[
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* \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
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* \f]
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* \ingroup features
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*/
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inline void
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solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix,
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const Eigen::Vector4f &point,
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Eigen::Vector4f &plane_parameters, float &curvature);
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/** \brief Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares
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* plane normal and surface curvature.
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* \param covariance_matrix the 3x3 covariance matrix
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* \param nx the resultant X component of the plane normal
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* \param ny the resultant Y component of the plane normal
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* \param nz the resultant Z component of the plane normal
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* \param curvature the estimated surface curvature as a measure of
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* \f[
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* \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
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* \f]
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* \ingroup features
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*/
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inline void
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solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix,
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float &nx, float &ny, float &nz, float &curvature);
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////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////
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/** \brief Feature represents the base feature class. Some generic 3D operations that
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* are applicable to all features are defined here as static methods.
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*
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* \attention
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* The convention for a feature descriptor is:
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* - if the nearest neighbors for the query point at which the descriptor is to be computed cannot be
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* determined, the descriptor values will be set to NaN (not a number)
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* - it is impossible to estimate a feature descriptor for a point that doesn't have finite 3D coordinates.
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* Therefore, any point that has NaN data on x, y, or z, will most likely have its descriptor set to NaN.
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*
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* \author Radu B. Rusu
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* \ingroup features
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*/
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template <typename PointInT, typename PointOutT>
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class Feature : public PCLBase<PointInT>
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{
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public:
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using PCLBase<PointInT>::indices_;
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using PCLBase<PointInT>::input_;
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using BaseClass = PCLBase<PointInT>;
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using Ptr = shared_ptr< Feature<PointInT, PointOutT> >;
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using ConstPtr = shared_ptr< const Feature<PointInT, PointOutT> >;
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using KdTree = pcl::search::Search<PointInT>;
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using KdTreePtr = typename KdTree::Ptr;
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using PointCloudIn = pcl::PointCloud<PointInT>;
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using PointCloudInPtr = typename PointCloudIn::Ptr;
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using PointCloudInConstPtr = typename PointCloudIn::ConstPtr;
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using PointCloudOut = pcl::PointCloud<PointOutT>;
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using SearchMethod = std::function<int (std::size_t, double, pcl::Indices &, std::vector<float> &)>;
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using SearchMethodSurface = std::function<int (const PointCloudIn &cloud, std::size_t index, double, pcl::Indices &, std::vector<float> &)>;
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public:
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/** \brief Empty constructor. */
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Feature () :
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feature_name_ (), search_method_surface_ (),
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surface_(), tree_(),
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search_parameter_(0), search_radius_(0), k_(0),
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fake_surface_(false)
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{}
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/** \brief Empty destructor */
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virtual ~Feature () {}
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/** \brief Provide a pointer to a dataset to add additional information
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* to estimate the features for every point in the input dataset. This
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* is optional, if this is not set, it will only use the data in the
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* input cloud to estimate the features. This is useful when you only
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* need to compute the features for a downsampled cloud.
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* \param[in] cloud a pointer to a PointCloud message
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*/
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inline void
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setSearchSurface (const PointCloudInConstPtr &cloud)
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{
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surface_ = cloud;
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fake_surface_ = false;
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//use_surface_ = true;
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}
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/** \brief Get a pointer to the surface point cloud dataset. */
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inline PointCloudInConstPtr
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getSearchSurface () const
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{
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return (surface_);
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}
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/** \brief Provide a pointer to the search object.
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* \param[in] tree a pointer to the spatial search object.
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*/
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inline void
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setSearchMethod (const KdTreePtr &tree) { tree_ = tree; }
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/** \brief Get a pointer to the search method used. */
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inline KdTreePtr
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getSearchMethod () const
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{
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return (tree_);
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}
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/** \brief Get the internal search parameter. */
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inline double
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getSearchParameter () const
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{
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return (search_parameter_);
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}
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/** \brief Set the number of k nearest neighbors to use for the feature estimation.
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* \param[in] k the number of k-nearest neighbors
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*/
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inline void
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setKSearch (int k) { k_ = k; }
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/** \brief get the number of k nearest neighbors used for the feature estimation. */
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inline int
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getKSearch () const
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{
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return (k_);
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}
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/** \brief Set the sphere radius that is to be used for determining the nearest neighbors used for the feature
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* estimation.
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* \param[in] radius the sphere radius used as the maximum distance to consider a point a neighbor
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*/
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inline void
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setRadiusSearch (double radius)
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{
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search_radius_ = radius;
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}
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/** \brief Get the sphere radius used for determining the neighbors. */
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inline double
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getRadiusSearch () const
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{
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return (search_radius_);
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}
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/** \brief Base method for feature estimation for all points given in
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* <setInputCloud (), setIndices ()> using the surface in setSearchSurface ()
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* and the spatial locator in setSearchMethod ()
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* \param[out] output the resultant point cloud model dataset containing the estimated features
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*/
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void
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compute (PointCloudOut &output);
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protected:
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/** \brief The feature name. */
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std::string feature_name_;
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/** \brief The search method template for points. */
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SearchMethodSurface search_method_surface_;
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/** \brief An input point cloud describing the surface that is to be used
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* for nearest neighbors estimation.
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*/
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PointCloudInConstPtr surface_;
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/** \brief A pointer to the spatial search object. */
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KdTreePtr tree_;
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/** \brief The actual search parameter (from either \a search_radius_ or \a k_). */
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double search_parameter_;
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/** \brief The nearest neighbors search radius for each point. */
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double search_radius_;
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/** \brief The number of K nearest neighbors to use for each point. */
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int k_;
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/** \brief Get a string representation of the name of this class. */
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inline const std::string&
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getClassName () const { return (feature_name_); }
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/** \brief This method should get called before starting the actual computation. */
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virtual bool
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initCompute ();
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/** \brief This method should get called after ending the actual computation. */
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virtual bool
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deinitCompute ();
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/** \brief If no surface is given, we use the input PointCloud as the surface. */
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bool fake_surface_;
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/** \brief Search for k-nearest neighbors using the spatial locator from
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* \a setSearchmethod, and the given surface from \a setSearchSurface.
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* \param[in] index the index of the query point
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* \param[in] parameter the search parameter (either k or radius)
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* \param[out] indices the resultant vector of indices representing the k-nearest neighbors
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* \param[out] distances the resultant vector of distances representing the distances from the query point to the
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* k-nearest neighbors
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*
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* \return the number of neighbors found. If no neighbors are found or an error occurred, return 0.
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*/
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inline int
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searchForNeighbors (std::size_t index, double parameter,
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pcl::Indices &indices, std::vector<float> &distances) const
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{
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return (search_method_surface_ (*input_, index, parameter, indices, distances));
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}
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/** \brief Search for k-nearest neighbors using the spatial locator from
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* \a setSearchmethod, and the given surface from \a setSearchSurface.
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* \param[in] cloud the query point cloud
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* \param[in] index the index of the query point in \a cloud
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* \param[in] parameter the search parameter (either k or radius)
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* \param[out] indices the resultant vector of indices representing the k-nearest neighbors
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* \param[out] distances the resultant vector of distances representing the distances from the query point to the
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* k-nearest neighbors
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*
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* \return the number of neighbors found. If no neighbors are found or an error occurred, return 0.
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*/
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inline int
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searchForNeighbors (const PointCloudIn &cloud, std::size_t index, double parameter,
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pcl::Indices &indices, std::vector<float> &distances) const
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{
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return (search_method_surface_ (cloud, index, parameter, indices, distances));
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}
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private:
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/** \brief Abstract feature estimation method.
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* \param[out] output the resultant features
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*/
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virtual void
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computeFeature (PointCloudOut &output) = 0;
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public:
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PCL_MAKE_ALIGNED_OPERATOR_NEW
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};
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////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT>
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class FeatureFromNormals : public Feature<PointInT, PointOutT>
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{
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using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
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using PointCloudInPtr = typename PointCloudIn::Ptr;
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using PointCloudInConstPtr = typename PointCloudIn::ConstPtr;
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using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
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public:
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using PointCloudN = pcl::PointCloud<PointNT>;
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using PointCloudNPtr = typename PointCloudN::Ptr;
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using PointCloudNConstPtr = typename PointCloudN::ConstPtr;
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using Ptr = shared_ptr< FeatureFromNormals<PointInT, PointNT, PointOutT> >;
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using ConstPtr = shared_ptr< const FeatureFromNormals<PointInT, PointNT, PointOutT> >;
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// Members derived from the base class
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using Feature<PointInT, PointOutT>::input_;
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using Feature<PointInT, PointOutT>::surface_;
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using Feature<PointInT, PointOutT>::getClassName;
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/** \brief Empty constructor. */
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FeatureFromNormals () : normals_ () {}
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/** \brief Empty destructor */
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virtual ~FeatureFromNormals () {}
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/** \brief Provide a pointer to the input dataset that contains the point normals of
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* the XYZ dataset.
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* In case of search surface is set to be different from the input cloud,
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* normals should correspond to the search surface, not the input cloud!
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* \param[in] normals the const boost shared pointer to a PointCloud of normals.
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* By convention, L2 norm of each normal should be 1.
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*/
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inline void
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setInputNormals (const PointCloudNConstPtr &normals) { normals_ = normals; }
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/** \brief Get a pointer to the normals of the input XYZ point cloud dataset. */
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inline PointCloudNConstPtr
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getInputNormals () const { return (normals_); }
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protected:
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/** \brief A pointer to the input dataset that contains the point normals of the XYZ
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* dataset.
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*/
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PointCloudNConstPtr normals_;
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/** \brief This method should get called before starting the actual computation. */
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virtual bool
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initCompute ();
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public:
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PCL_MAKE_ALIGNED_OPERATOR_NEW
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};
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////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointLT, typename PointOutT>
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class FeatureFromLabels : public Feature<PointInT, PointOutT>
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{
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using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
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using PointCloudInPtr = typename PointCloudIn::Ptr;
|
||
|
|
using PointCloudInConstPtr = typename PointCloudIn::ConstPtr;
|
||
|
|
|
||
|
|
using PointCloudL = pcl::PointCloud<PointLT>;
|
||
|
|
using PointCloudNPtr = typename PointCloudL::Ptr;
|
||
|
|
using PointCloudLConstPtr = typename PointCloudL::ConstPtr;
|
||
|
|
|
||
|
|
using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
|
||
|
|
|
||
|
|
public:
|
||
|
|
using Ptr = shared_ptr< FeatureFromLabels<PointInT, PointLT, PointOutT> >;
|
||
|
|
using ConstPtr = shared_ptr< const FeatureFromLabels<PointInT, PointLT, PointOutT> >;
|
||
|
|
|
||
|
|
// Members derived from the base class
|
||
|
|
using Feature<PointInT, PointOutT>::input_;
|
||
|
|
using Feature<PointInT, PointOutT>::surface_;
|
||
|
|
using Feature<PointInT, PointOutT>::getClassName;
|
||
|
|
using Feature<PointInT, PointOutT>::k_;
|
||
|
|
|
||
|
|
/** \brief Empty constructor. */
|
||
|
|
FeatureFromLabels () : labels_ ()
|
||
|
|
{
|
||
|
|
k_ = 1; // Search tree is not always used here.
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Empty destructor */
|
||
|
|
virtual ~FeatureFromLabels () {}
|
||
|
|
|
||
|
|
/** \brief Provide a pointer to the input dataset that contains the point labels of
|
||
|
|
* the XYZ dataset.
|
||
|
|
* In case of search surface is set to be different from the input cloud,
|
||
|
|
* labels should correspond to the search surface, not the input cloud!
|
||
|
|
* \param[in] labels the const boost shared pointer to a PointCloud of labels.
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setInputLabels (const PointCloudLConstPtr &labels)
|
||
|
|
{
|
||
|
|
labels_ = labels;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Get a pointer to the labels of the input XYZ point cloud dataset. */
|
||
|
|
inline PointCloudLConstPtr
|
||
|
|
getInputLabels () const
|
||
|
|
{
|
||
|
|
return (labels_);
|
||
|
|
}
|
||
|
|
|
||
|
|
protected:
|
||
|
|
/** \brief A pointer to the input dataset that contains the point labels of the XYZ
|
||
|
|
* dataset.
|
||
|
|
*/
|
||
|
|
PointCloudLConstPtr labels_;
|
||
|
|
|
||
|
|
/** \brief This method should get called before starting the actual computation. */
|
||
|
|
virtual bool
|
||
|
|
initCompute ();
|
||
|
|
|
||
|
|
public:
|
||
|
|
PCL_MAKE_ALIGNED_OPERATOR_NEW
|
||
|
|
};
|
||
|
|
|
||
|
|
////////////////////////////////////////////////////////////////////////////////////////////
|
||
|
|
////////////////////////////////////////////////////////////////////////////////////////////
|
||
|
|
////////////////////////////////////////////////////////////////////////////////////////////
|
||
|
|
/** \brief FeatureWithLocalReferenceFrames provides a public interface for descriptor
|
||
|
|
* extractor classes which need a local reference frame at each input keypoint.
|
||
|
|
*
|
||
|
|
* \attention
|
||
|
|
* This interface is for backward compatibility with existing code and in the future it could be
|
||
|
|
* merged with pcl::Feature. Subclasses should call the protected method initLocalReferenceFrames ()
|
||
|
|
* to correctly initialize the frames_ member.
|
||
|
|
*
|
||
|
|
* \author Nicola Fioraio
|
||
|
|
* \ingroup features
|
||
|
|
*/
|
||
|
|
template <typename PointInT, typename PointRFT>
|
||
|
|
class FeatureWithLocalReferenceFrames
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
using PointCloudLRF = pcl::PointCloud<PointRFT>;
|
||
|
|
using PointCloudLRFPtr = typename PointCloudLRF::Ptr;
|
||
|
|
using PointCloudLRFConstPtr = typename PointCloudLRF::ConstPtr;
|
||
|
|
|
||
|
|
/** \brief Empty constructor. */
|
||
|
|
FeatureWithLocalReferenceFrames () : frames_ (), frames_never_defined_ (true) {}
|
||
|
|
|
||
|
|
/** \brief Empty destructor. */
|
||
|
|
virtual ~FeatureWithLocalReferenceFrames () {}
|
||
|
|
|
||
|
|
/** \brief Provide a pointer to the input dataset that contains the local
|
||
|
|
* reference frames of the XYZ dataset.
|
||
|
|
* In case of search surface is set to be different from the input cloud,
|
||
|
|
* local reference frames should correspond to the input cloud, not the search surface!
|
||
|
|
* \param[in] frames the const boost shared pointer to a PointCloud of reference frames.
|
||
|
|
*/
|
||
|
|
inline void
|
||
|
|
setInputReferenceFrames (const PointCloudLRFConstPtr &frames)
|
||
|
|
{
|
||
|
|
frames_ = frames;
|
||
|
|
frames_never_defined_ = false;
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief Get a pointer to the local reference frames. */
|
||
|
|
inline PointCloudLRFConstPtr
|
||
|
|
getInputReferenceFrames () const
|
||
|
|
{
|
||
|
|
return (frames_);
|
||
|
|
}
|
||
|
|
|
||
|
|
protected:
|
||
|
|
/** \brief A boost shared pointer to the local reference frames. */
|
||
|
|
PointCloudLRFConstPtr frames_;
|
||
|
|
/** \brief The user has never set the frames. */
|
||
|
|
bool frames_never_defined_;
|
||
|
|
|
||
|
|
/** \brief Check if frames_ has been correctly initialized and compute it if needed.
|
||
|
|
* \param input the subclass' input cloud dataset.
|
||
|
|
* \param lrf_estimation a pointer to a local reference frame estimation class to be used as default.
|
||
|
|
* \return true if frames_ has been correctly initialized.
|
||
|
|
*/
|
||
|
|
using LRFEstimationPtr = typename Feature<PointInT, PointRFT>::Ptr;
|
||
|
|
virtual bool
|
||
|
|
initLocalReferenceFrames (const std::size_t& indices_size,
|
||
|
|
const LRFEstimationPtr& lrf_estimation = LRFEstimationPtr());
|
||
|
|
};
|
||
|
|
}
|
||
|
|
|
||
|
|
#include <pcl/features/impl/feature.hpp>
|