249 lines
9.7 KiB
C++
249 lines
9.7 KiB
C++
/*
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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) 2009-2012, 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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*/
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#pragma once
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// PCL includes
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#include <pcl/registration/icp.h>
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namespace pcl {
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/** \brief @b JointIterativeClosestPoint extends ICP to multiple frames which
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* share the same transform. This is particularly useful when solving for
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* camera extrinsics using multiple observations. When given a single pair of
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* clouds, this reduces to vanilla ICP.
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*
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* \author Stephen Miller
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* \ingroup registration
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*/
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template <typename PointSource, typename PointTarget, typename Scalar = float>
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class JointIterativeClosestPoint
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: public IterativeClosestPoint<PointSource, PointTarget, Scalar> {
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public:
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using PointCloudSource = typename IterativeClosestPoint<PointSource,
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PointTarget,
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Scalar>::PointCloudSource;
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using PointCloudSourcePtr = typename PointCloudSource::Ptr;
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using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
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using PointCloudTarget = typename IterativeClosestPoint<PointSource,
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PointTarget,
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Scalar>::PointCloudTarget;
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using PointCloudTargetPtr = typename PointCloudTarget::Ptr;
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using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr;
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using KdTree = pcl::search::KdTree<PointTarget>;
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using KdTreePtr = typename KdTree::Ptr;
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using KdTreeReciprocal = pcl::search::KdTree<PointSource>;
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using KdTreeReciprocalPtr = typename KdTree::Ptr;
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using PointIndicesPtr = PointIndices::Ptr;
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using PointIndicesConstPtr = PointIndices::ConstPtr;
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using Ptr = shared_ptr<JointIterativeClosestPoint<PointSource, PointTarget, Scalar>>;
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using ConstPtr =
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shared_ptr<const JointIterativeClosestPoint<PointSource, PointTarget, Scalar>>;
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using CorrespondenceEstimation =
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pcl::registration::CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>;
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using CorrespondenceEstimationPtr = typename CorrespondenceEstimation::Ptr;
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using CorrespondenceEstimationConstPtr = typename CorrespondenceEstimation::ConstPtr;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::reg_name_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::getClassName;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::setInputSource;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::input_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::indices_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::target_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::nr_iterations_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::max_iterations_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
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previous_transformation_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::final_transformation_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::transformation_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
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transformation_epsilon_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::converged_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::corr_dist_threshold_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::inlier_threshold_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
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min_number_correspondences_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::update_visualizer_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
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euclidean_fitness_epsilon_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::correspondences_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
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transformation_estimation_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
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correspondence_estimation_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
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correspondence_rejectors_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::
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use_reciprocal_correspondence_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::convergence_criteria_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::source_has_normals_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::target_has_normals_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::need_source_blob_;
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using IterativeClosestPoint<PointSource, PointTarget, Scalar>::need_target_blob_;
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using Matrix4 =
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typename IterativeClosestPoint<PointSource, PointTarget, Scalar>::Matrix4;
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/** \brief Empty constructor. */
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JointIterativeClosestPoint()
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{
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IterativeClosestPoint<PointSource, PointTarget, Scalar>();
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reg_name_ = "JointIterativeClosestPoint";
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};
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/** \brief Empty destructor */
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~JointIterativeClosestPoint() {}
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/** \brief Provide a pointer to the input source
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* (e.g., the point cloud that we want to align to the target)
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*/
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void
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setInputSource(const PointCloudSourceConstPtr& /*cloud*/) override
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{
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PCL_WARN("[pcl::%s::setInputSource] Warning; JointIterativeClosestPoint expects "
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"multiple clouds. Please use addInputSource.\n",
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getClassName().c_str());
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return;
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}
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/** \brief Add a source cloud to the joint solver
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*
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* \param[in] cloud source cloud
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*/
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inline void
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addInputSource(const PointCloudSourceConstPtr& cloud)
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{
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// Set the parent InputSource, just to get all cached values (e.g. the existence of
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// normals).
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if (sources_.empty())
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IterativeClosestPoint<PointSource, PointTarget, Scalar>::setInputSource(cloud);
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sources_.push_back(cloud);
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}
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/** \brief Provide a pointer to the input target
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* (e.g., the point cloud that we want to align to the target)
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*/
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void
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setInputTarget(const PointCloudTargetConstPtr& /*cloud*/) override
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{
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PCL_WARN("[pcl::%s::setInputTarget] Warning; JointIterativeClosestPoint expects "
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"multiple clouds. Please use addInputTarget.\n",
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getClassName().c_str());
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return;
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}
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/** \brief Add a target cloud to the joint solver
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*
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* \param[in] cloud target cloud
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*/
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inline void
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addInputTarget(const PointCloudTargetConstPtr& cloud)
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{
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// Set the parent InputTarget, just to get all cached values (e.g. the existence of
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// normals).
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if (targets_.empty())
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IterativeClosestPoint<PointSource, PointTarget, Scalar>::setInputTarget(cloud);
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targets_.push_back(cloud);
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}
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/** \brief Add a manual correspondence estimator
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* If you choose to do this, you must add one for each
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* input source / target pair. They do not need to have trees
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* or input clouds set ahead of time.
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*
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* \param[in] ce Correspondence estimation
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*/
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inline void
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addCorrespondenceEstimation(CorrespondenceEstimationPtr ce)
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{
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correspondence_estimations_.push_back(ce);
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}
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/** \brief Reset my list of input sources
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*/
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inline void
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clearInputSources()
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{
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sources_.clear();
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}
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/** \brief Reset my list of input targets
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*/
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inline void
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clearInputTargets()
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{
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targets_.clear();
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}
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/** \brief Reset my list of correspondence estimation methods.
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*/
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inline void
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clearCorrespondenceEstimations()
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{
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correspondence_estimations_.clear();
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}
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protected:
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/** \brief Rigid transformation computation method with initial guess.
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* \param output the transformed input point cloud dataset using the rigid
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* transformation found \param guess the initial guess of the transformation to
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* compute
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*/
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void
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computeTransformation(PointCloudSource& output, const Matrix4& guess) override;
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/** \brief Looks at the Estimators and Rejectors and determines whether their
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* blob-setter methods need to be called */
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void
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determineRequiredBlobData() override;
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std::vector<PointCloudSourceConstPtr> sources_;
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std::vector<PointCloudTargetConstPtr> targets_;
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std::vector<CorrespondenceEstimationPtr> correspondence_estimations_;
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};
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} // namespace pcl
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#include <pcl/registration/impl/joint_icp.hpp>
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