351 lines
13 KiB
C++
351 lines
13 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) 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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#include <pcl/registration/transformation_estimation.h>
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#include <pcl/registration/warp_point_rigid.h>
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#include <pcl/memory.h>
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namespace pcl {
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namespace registration {
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/** @b TransformationEstimationLM implements Levenberg Marquardt-based
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* estimation of the transformation aligning the given correspondences.
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*
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* \note The class is templated on the source and target point types as well as on the
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* output scalar of the transformation matrix (i.e., float or double). Default: float.
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* \author Radu B. Rusu
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* \ingroup registration
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*/
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template <typename PointSource, typename PointTarget, typename MatScalar = float>
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class TransformationEstimationLM
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: public TransformationEstimation<PointSource, PointTarget, MatScalar> {
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using PointCloudSource = pcl::PointCloud<PointSource>;
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using PointCloudSourcePtr = typename PointCloudSource::Ptr;
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using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
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using PointCloudTarget = pcl::PointCloud<PointTarget>;
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using PointIndicesPtr = PointIndices::Ptr;
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using PointIndicesConstPtr = PointIndices::ConstPtr;
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public:
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using Ptr =
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shared_ptr<TransformationEstimationLM<PointSource, PointTarget, MatScalar>>;
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using ConstPtr =
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shared_ptr<const TransformationEstimationLM<PointSource, PointTarget, MatScalar>>;
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using VectorX = Eigen::Matrix<MatScalar, Eigen::Dynamic, 1>;
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using Vector4 = Eigen::Matrix<MatScalar, 4, 1>;
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using Matrix4 =
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typename TransformationEstimation<PointSource, PointTarget, MatScalar>::Matrix4;
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/** \brief Constructor. */
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TransformationEstimationLM();
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/** \brief Copy constructor.
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* \param[in] src the TransformationEstimationLM object to copy into this
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*/
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TransformationEstimationLM(const TransformationEstimationLM& src)
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: tmp_src_(src.tmp_src_)
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, tmp_tgt_(src.tmp_tgt_)
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, tmp_idx_src_(src.tmp_idx_src_)
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, tmp_idx_tgt_(src.tmp_idx_tgt_)
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, warp_point_(src.warp_point_){};
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/** \brief Copy operator.
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* \param[in] src the TransformationEstimationLM object to copy into this
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*/
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TransformationEstimationLM&
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operator=(const TransformationEstimationLM& src)
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{
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tmp_src_ = src.tmp_src_;
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tmp_tgt_ = src.tmp_tgt_;
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tmp_idx_src_ = src.tmp_idx_src_;
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tmp_idx_tgt_ = src.tmp_idx_tgt_;
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warp_point_ = src.warp_point_;
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}
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/** \brief Destructor. */
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~TransformationEstimationLM(){};
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/** \brief Estimate a rigid rotation transformation between a source and a target
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* point cloud using LM. \param[in] cloud_src the source point cloud dataset
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* \param[in] cloud_tgt the target point cloud dataset
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* \param[out] transformation_matrix the resultant transformation matrix
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*/
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inline void
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estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
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const pcl::PointCloud<PointTarget>& cloud_tgt,
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Matrix4& transformation_matrix) const override;
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/** \brief Estimate a rigid rotation transformation between a source and a target
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* point cloud using LM. \param[in] cloud_src the source point cloud dataset
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* \param[in] indices_src the vector of indices describing the points of interest in
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* \a cloud_src
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* \param[in] cloud_tgt the target point cloud dataset
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* \param[out] transformation_matrix the resultant transformation matrix
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*/
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inline void
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estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
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const pcl::Indices& indices_src,
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const pcl::PointCloud<PointTarget>& cloud_tgt,
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Matrix4& transformation_matrix) const override;
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/** \brief Estimate a rigid rotation transformation between a source and a target
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* point cloud using LM. \param[in] cloud_src the source point cloud dataset
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* \param[in] indices_src the vector of indices describing the points of interest in
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* \a cloud_src
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* \param[in] cloud_tgt the target point cloud dataset
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* \param[in] indices_tgt the vector of indices describing the correspondences of the
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* interest points from \a indices_src
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* \param[out] transformation_matrix the resultant transformation matrix
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*/
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inline void
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estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
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const pcl::Indices& indices_src,
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const pcl::PointCloud<PointTarget>& cloud_tgt,
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const pcl::Indices& indices_tgt,
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Matrix4& transformation_matrix) const override;
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/** \brief Estimate a rigid rotation transformation between a source and a target
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* point cloud using LM. \param[in] cloud_src the source point cloud dataset
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* \param[in] cloud_tgt the target point cloud dataset
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* \param[in] correspondences the vector of correspondences between source and target
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* point cloud \param[out] transformation_matrix the resultant transformation matrix
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*/
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inline void
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estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
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const pcl::PointCloud<PointTarget>& cloud_tgt,
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const pcl::Correspondences& correspondences,
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Matrix4& transformation_matrix) const override;
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/** \brief Set the function we use to warp points. Defaults to rigid 6D warp.
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* \param[in] warp_fcn a shared pointer to an object that warps points
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*/
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void
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setWarpFunction(
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const typename WarpPointRigid<PointSource, PointTarget, MatScalar>::Ptr& warp_fcn)
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{
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warp_point_ = warp_fcn;
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}
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protected:
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/** \brief Compute the distance between a source point and its corresponding target
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* point \param[in] p_src The source point \param[in] p_tgt The target point \return
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* The distance between \a p_src and \a p_tgt
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*
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* \note Older versions of PCL used this method internally for calculating the
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* optimization gradient. Since PCL 1.7, a switch has been made to the
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* computeDistance method using Vector4 types instead. This method is only
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* kept for API compatibility reasons.
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*/
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virtual MatScalar
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computeDistance(const PointSource& p_src, const PointTarget& p_tgt) const
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{
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Vector4 s(p_src.x, p_src.y, p_src.z, 0);
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Vector4 t(p_tgt.x, p_tgt.y, p_tgt.z, 0);
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return ((s - t).norm());
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}
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/** \brief Compute the distance between a source point and its corresponding target
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* point \param[in] p_src The source point \param[in] p_tgt The target point \return
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* The distance between \a p_src and \a p_tgt
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*
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* \note A different distance function can be defined by creating a subclass of
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* TransformationEstimationLM and overriding this method.
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* (See \a TransformationEstimationPointToPlane)
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*/
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virtual MatScalar
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computeDistance(const Vector4& p_src, const PointTarget& p_tgt) const
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{
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Vector4 t(p_tgt.x, p_tgt.y, p_tgt.z, 0);
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return ((p_src - t).norm());
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}
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/** \brief Temporary pointer to the source dataset. */
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mutable const PointCloudSource* tmp_src_;
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/** \brief Temporary pointer to the target dataset. */
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mutable const PointCloudTarget* tmp_tgt_;
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/** \brief Temporary pointer to the source dataset indices. */
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mutable const pcl::Indices* tmp_idx_src_;
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/** \brief Temporary pointer to the target dataset indices. */
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mutable const pcl::Indices* tmp_idx_tgt_;
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/** \brief The parameterized function used to warp the source to the target. */
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typename pcl::registration::WarpPointRigid<PointSource, PointTarget, MatScalar>::Ptr
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warp_point_;
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/** Base functor all the models that need non linear optimization must
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* define their own one and implement operator() (const Eigen::VectorXd& x,
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* Eigen::VectorXd& fvec) or operator() (const Eigen::VectorXf& x, Eigen::VectorXf&
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* fvec) depending on the chosen _Scalar
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*/
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template <typename _Scalar, int NX = Eigen::Dynamic, int NY = Eigen::Dynamic>
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struct Functor {
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using Scalar = _Scalar;
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enum { InputsAtCompileTime = NX, ValuesAtCompileTime = NY };
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using InputType = Eigen::Matrix<_Scalar, InputsAtCompileTime, 1>;
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using ValueType = Eigen::Matrix<_Scalar, ValuesAtCompileTime, 1>;
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using JacobianType =
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Eigen::Matrix<_Scalar, ValuesAtCompileTime, InputsAtCompileTime>;
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/** \brief Empty Constructor. */
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Functor() : m_data_points_(ValuesAtCompileTime) {}
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/** \brief Constructor
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* \param[in] m_data_points number of data points to evaluate.
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*/
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Functor(int m_data_points) : m_data_points_(m_data_points) {}
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/** \brief Destructor. */
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virtual ~Functor() {}
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/** \brief Get the number of values. */
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int
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values() const
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{
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return (m_data_points_);
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}
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protected:
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int m_data_points_;
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};
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struct OptimizationFunctor : public Functor<MatScalar> {
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using Functor<MatScalar>::values;
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/** Functor constructor
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* \param[in] m_data_points the number of data points to evaluate
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* \param[in,out] estimator pointer to the estimator object
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*/
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OptimizationFunctor(int m_data_points, const TransformationEstimationLM* estimator)
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: Functor<MatScalar>(m_data_points), estimator_(estimator)
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{}
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/** Copy constructor
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* \param[in] src the optimization functor to copy into this
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*/
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inline OptimizationFunctor(const OptimizationFunctor& src)
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: Functor<MatScalar>(src.m_data_points_), estimator_()
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{
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*this = src;
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}
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/** Copy operator
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* \param[in] src the optimization functor to copy into this
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*/
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inline OptimizationFunctor&
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operator=(const OptimizationFunctor& src)
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{
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Functor<MatScalar>::operator=(src);
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estimator_ = src.estimator_;
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return (*this);
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}
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/** \brief Destructor. */
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~OptimizationFunctor() {}
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/** Fill fvec from x. For the current state vector x fill the f values
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* \param[in] x state vector
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* \param[out] fvec f values vector
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*/
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int
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operator()(const VectorX& x, VectorX& fvec) const;
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const TransformationEstimationLM<PointSource, PointTarget, MatScalar>* estimator_;
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};
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struct OptimizationFunctorWithIndices : public Functor<MatScalar> {
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using Functor<MatScalar>::values;
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/** Functor constructor
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* \param[in] m_data_points the number of data points to evaluate
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* \param[in,out] estimator pointer to the estimator object
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*/
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OptimizationFunctorWithIndices(int m_data_points,
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const TransformationEstimationLM* estimator)
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: Functor<MatScalar>(m_data_points), estimator_(estimator)
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{}
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/** Copy constructor
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* \param[in] src the optimization functor to copy into this
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*/
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inline OptimizationFunctorWithIndices(const OptimizationFunctorWithIndices& src)
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: Functor<MatScalar>(src.m_data_points_), estimator_()
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{
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*this = src;
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}
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/** Copy operator
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* \param[in] src the optimization functor to copy into this
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*/
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inline OptimizationFunctorWithIndices&
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operator=(const OptimizationFunctorWithIndices& src)
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{
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Functor<MatScalar>::operator=(src);
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estimator_ = src.estimator_;
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return (*this);
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}
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/** \brief Destructor. */
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~OptimizationFunctorWithIndices() {}
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/** Fill fvec from x. For the current state vector x fill the f values
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* \param[in] x state vector
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* \param[out] fvec f values vector
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*/
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int
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operator()(const VectorX& x, VectorX& fvec) const;
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const TransformationEstimationLM<PointSource, PointTarget, MatScalar>* estimator_;
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};
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public:
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PCL_MAKE_ALIGNED_OPERATOR_NEW
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};
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} // namespace registration
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} // namespace pcl
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#include <pcl/registration/impl/transformation_estimation_lm.hpp>
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