122 lines
4.4 KiB
C
122 lines
4.4 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) 2010-2012, 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 <Eigen/Core> // for Matrix
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#include <pcl/memory.h>
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#include <pcl/pcl_macros.h>
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namespace pcl
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{
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/** \brief Calculates the weighted average and the covariance matrix
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*
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* A class to calculate the weighted average and the covariance matrix of a set of vectors with given weights.
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* The original data is not saved. Mean and covariance are calculated iteratively.
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* \author Bastian Steder
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* \ingroup common
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*/
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template <typename real, int dimension>
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class VectorAverage
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{
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public:
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using VectorType = Eigen::Matrix<real, dimension, 1>;
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using MatrixType = Eigen::Matrix<real, dimension, dimension>;
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//-----CONSTRUCTOR&DESTRUCTOR-----
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/** Constructor - dimension gives the size of the vectors to work with. */
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VectorAverage ();
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//-----METHODS-----
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/** Reset the object to work with a new data set */
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inline void
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reset ();
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/** Get the mean of the added vectors */
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inline const
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VectorType& getMean () const { return mean_;}
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/** Get the covariance matrix of the added vectors */
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inline const
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MatrixType& getCovariance () const { return covariance_;}
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/** Get the summed up weight of all added vectors */
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inline real
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getAccumulatedWeight () const { return accumulatedWeight_;}
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/** Get the number of added vectors */
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inline unsigned int
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getNoOfSamples () { return noOfSamples_;}
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/** Add a new sample */
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inline void
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add (const VectorType& sample, real weight=1.0);
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/** Do Principal component analysis */
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inline void
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doPCA (VectorType& eigen_values, VectorType& eigen_vector1,
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VectorType& eigen_vector2, VectorType& eigen_vector3) const;
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/** Do Principal component analysis */
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inline void
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doPCA (VectorType& eigen_values) const;
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/** Get the eigenvector corresponding to the smallest eigenvalue */
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inline void
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getEigenVector1 (VectorType& eigen_vector1) const;
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PCL_MAKE_ALIGNED_OPERATOR_NEW
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//-----VARIABLES-----
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protected:
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//-----METHODS-----
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//-----VARIABLES-----
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unsigned int noOfSamples_ = 0;
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real accumulatedWeight_ = 0;
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VectorType mean_ = VectorType::Identity ();
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MatrixType covariance_ = MatrixType::Identity ();
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};
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using VectorAverage2f = VectorAverage<float, 2>;
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using VectorAverage3f = VectorAverage<float, 3>;
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using VectorAverage4f = VectorAverage<float, 4>;
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} // END namespace
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#include <pcl/common/impl/vector_average.hpp>
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