203 lines
8.8 KiB
C++
203 lines
8.8 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) 2010-2012, 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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#pragma once
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#include <pcl/common/eigen.h> // for computeRoots, eigen33
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#include <pcl/common/vector_average.h>
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#include <Eigen/Eigenvalues> // for SelfAdjointEigenSolver
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namespace pcl
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{
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template <typename real, int dimension>
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VectorAverage<real, dimension>::VectorAverage ()
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{
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reset();
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}
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template <typename real, int dimension>
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inline void VectorAverage<real, dimension>::reset()
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{
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noOfSamples_ = 0;
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accumulatedWeight_ = 0.0;
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mean_.fill(0);
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covariance_.fill(0);
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}
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template <typename real, int dimension>
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inline void VectorAverage<real, dimension>::add(const Eigen::Matrix<real, dimension, 1>& sample, real weight) {
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if (weight == 0.0f)
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return;
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++noOfSamples_;
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accumulatedWeight_ += weight;
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real alpha = weight/accumulatedWeight_;
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Eigen::Matrix<real, dimension, 1> diff = sample - mean_;
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covariance_ = (covariance_ + (diff * diff.transpose())*alpha)*(1.0f-alpha);
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mean_ += (diff)*alpha;
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//if (std::isnan(covariance_(0,0)))
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//{
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//std::cout << PVARN(weight);
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//exit(0);
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//}
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}
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template <typename real, int dimension>
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inline void VectorAverage<real, dimension>::doPCA(Eigen::Matrix<real, dimension, 1>& eigen_values, Eigen::Matrix<real, dimension, 1>& eigen_vector1,
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Eigen::Matrix<real, dimension, 1>& eigen_vector2, Eigen::Matrix<real, dimension, 1>& eigen_vector3) const
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{
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// The following step is necessary for cases where the values in the covariance matrix are small
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// In this case float accuracy is nor enough to calculate the eigenvalues and eigenvectors.
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//Eigen::Matrix<double, dimension, dimension> tmp_covariance = covariance_.template cast<double>();
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//Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, dimension, dimension> > ei_symm(tmp_covariance);
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//eigen_values = ei_symm.eigenvalues().template cast<real>();
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//Eigen::Matrix<real, dimension, dimension> eigen_vectors = ei_symm.eigenvectors().template cast<real>();
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//std::cout << "My covariance is \n"<<covariance_<<"\n";
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//std::cout << "My mean is \n"<<mean_<<"\n";
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//std::cout << "My Eigenvectors \n"<<eigen_vectors<<"\n";
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Eigen::SelfAdjointEigenSolver<Eigen::Matrix<real, dimension, dimension> > ei_symm(covariance_);
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eigen_values = ei_symm.eigenvalues();
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Eigen::Matrix<real, dimension, dimension> eigen_vectors = ei_symm.eigenvectors();
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eigen_vector1 = eigen_vectors.col(0);
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eigen_vector2 = eigen_vectors.col(1);
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eigen_vector3 = eigen_vectors.col(2);
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}
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template <typename real, int dimension>
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inline void VectorAverage<real, dimension>::doPCA(Eigen::Matrix<real, dimension, 1>& eigen_values) const
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{
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// The following step is necessary for cases where the values in the covariance matrix are small
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// In this case float accuracy is nor enough to calculate the eigenvalues and eigenvectors.
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//Eigen::Matrix<double, dimension, dimension> tmp_covariance = covariance_.template cast<double>();
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//Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, dimension, dimension> > ei_symm(tmp_covariance, false);
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//eigen_values = ei_symm.eigenvalues().template cast<real>();
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Eigen::SelfAdjointEigenSolver<Eigen::Matrix<real, dimension, dimension> > ei_symm(covariance_, false);
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eigen_values = ei_symm.eigenvalues();
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}
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template <typename real, int dimension>
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inline void VectorAverage<real, dimension>::getEigenVector1(Eigen::Matrix<real, dimension, 1>& eigen_vector1) const
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{
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// The following step is necessary for cases where the values in the covariance matrix are small
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// In this case float accuracy is nor enough to calculate the eigenvalues and eigenvectors.
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//Eigen::Matrix<double, dimension, dimension> tmp_covariance = covariance_.template cast<double>();
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//Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, dimension, dimension> > ei_symm(tmp_covariance);
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//eigen_values = ei_symm.eigenvalues().template cast<real>();
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//Eigen::Matrix<real, dimension, dimension> eigen_vectors = ei_symm.eigenvectors().template cast<real>();
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//std::cout << "My covariance is \n"<<covariance_<<"\n";
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//std::cout << "My mean is \n"<<mean_<<"\n";
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//std::cout << "My Eigenvectors \n"<<eigen_vectors<<"\n";
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Eigen::SelfAdjointEigenSolver<Eigen::Matrix<real, dimension, dimension> > ei_symm(covariance_);
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Eigen::Matrix<real, dimension, dimension> eigen_vectors = ei_symm.eigenvectors();
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eigen_vector1 = eigen_vectors.col(0);
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// Special cases for real=float & dimension=3 -> Partial specialization does not work with class templates. :( //
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/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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///////////
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// float //
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///////////
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template <>
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inline void VectorAverage<float, 3>::doPCA(Eigen::Matrix<float, 3, 1>& eigen_values, Eigen::Matrix<float, 3, 1>& eigen_vector1,
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Eigen::Matrix<float, 3, 1>& eigen_vector2, Eigen::Matrix<float, 3, 1>& eigen_vector3) const
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{
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//std::cout << "Using specialized 3x3 version of doPCA!\n";
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Eigen::Matrix<float, 3, 3> eigen_vectors;
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eigen33(covariance_, eigen_vectors, eigen_values);
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eigen_vector1 = eigen_vectors.col(0);
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eigen_vector2 = eigen_vectors.col(1);
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eigen_vector3 = eigen_vectors.col(2);
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}
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template <>
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inline void VectorAverage<float, 3>::doPCA(Eigen::Matrix<float, 3, 1>& eigen_values) const
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{
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//std::cout << "Using specialized 3x3 version of doPCA!\n";
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computeRoots (covariance_, eigen_values);
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}
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template <>
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inline void VectorAverage<float, 3>::getEigenVector1(Eigen::Matrix<float, 3, 1>& eigen_vector1) const
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{
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//std::cout << "Using specialized 3x3 version of doPCA!\n";
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Eigen::Vector3f::Scalar eigen_value;
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Eigen::Vector3f eigen_vector;
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eigen33(covariance_, eigen_value, eigen_vector);
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eigen_vector1 = eigen_vector;
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}
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////////////
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// double //
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////////////
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template <>
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inline void VectorAverage<double, 3>::doPCA(Eigen::Matrix<double, 3, 1>& eigen_values, Eigen::Matrix<double, 3, 1>& eigen_vector1,
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Eigen::Matrix<double, 3, 1>& eigen_vector2, Eigen::Matrix<double, 3, 1>& eigen_vector3) const
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{
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//std::cout << "Using specialized 3x3 version of doPCA!\n";
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Eigen::Matrix<double, 3, 3> eigen_vectors;
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eigen33(covariance_, eigen_vectors, eigen_values);
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eigen_vector1 = eigen_vectors.col(0);
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eigen_vector2 = eigen_vectors.col(1);
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eigen_vector3 = eigen_vectors.col(2);
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}
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template <>
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inline void VectorAverage<double, 3>::doPCA(Eigen::Matrix<double, 3, 1>& eigen_values) const
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{
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//std::cout << "Using specialized 3x3 version of doPCA!\n";
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computeRoots (covariance_, eigen_values);
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}
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template <>
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inline void VectorAverage<double, 3>::getEigenVector1(Eigen::Matrix<double, 3, 1>& eigen_vector1) const
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{
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//std::cout << "Using specialized 3x3 version of doPCA!\n";
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Eigen::Vector3d::Scalar eigen_value;
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Eigen::Vector3d eigen_vector;
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eigen33(covariance_, eigen_value, eigen_vector);
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eigen_vector1 = eigen_vector;
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}
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} // namespace pcl
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