207 lines
8.8 KiB
C
207 lines
8.8 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) 2011, Alexandru-Eugen Ichim
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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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#pragma once
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#include <pcl/pcl_base.h>
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#include <pcl/features/feature.h>
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#include <pcl/point_representation.h>
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#include <pcl/common/norms.h>
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#include <list>
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namespace pcl
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{
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/** \brief Generic class for extracting the persistent features from an input point cloud
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* It can be given any Feature estimator instance and will compute the features of the input
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* over a multiscale representation of the cloud and output the unique ones over those scales.
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*
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* Please refer to the following publication for more details:
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* Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, and Michael Beetz
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* Persistent Point Feature Histograms for 3D Point Clouds
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* Proceedings of the 10th International Conference on Intelligent Autonomous Systems (IAS-10)
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* 2008, Baden-Baden, Germany
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*
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* \author Alexandru-Eugen Ichim
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*/
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template <typename PointSource, typename PointFeature>
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class MultiscaleFeaturePersistence : public PCLBase<PointSource>
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{
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public:
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using Ptr = shared_ptr<MultiscaleFeaturePersistence<PointSource, PointFeature> >;
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using ConstPtr = shared_ptr<const MultiscaleFeaturePersistence<PointSource, PointFeature> >;
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using FeatureCloud = pcl::PointCloud<PointFeature>;
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using FeatureCloudPtr = typename pcl::PointCloud<PointFeature>::Ptr;
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using FeatureEstimatorPtr = typename pcl::Feature<PointSource, PointFeature>::Ptr;
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using FeatureRepresentationConstPtr = typename pcl::PointRepresentation<PointFeature>::ConstPtr;
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using pcl::PCLBase<PointSource>::input_;
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/** \brief Empty constructor */
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MultiscaleFeaturePersistence ();
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/** \brief Empty destructor */
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~MultiscaleFeaturePersistence () {}
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/** \brief Method that calls computeFeatureAtScale () for each scale parameter */
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void
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computeFeaturesAtAllScales ();
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/** \brief Central function that computes the persistent features
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* \param output_features a cloud containing the persistent features
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* \param output_indices vector containing the indices of the points in the input cloud
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* that have persistent features, under a one-to-one correspondence with the output_features cloud
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*/
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void
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determinePersistentFeatures (FeatureCloud &output_features,
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pcl::IndicesPtr &output_indices);
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/** \brief Method for setting the scale parameters for the algorithm
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* \param scale_values vector of scales to determine the characteristic of each scaling step
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*/
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inline void
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setScalesVector (std::vector<float> &scale_values) { scale_values_ = scale_values; }
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/** \brief Method for getting the scale parameters vector */
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inline std::vector<float>
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getScalesVector () { return scale_values_; }
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/** \brief Setter method for the feature estimator
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* \param feature_estimator pointer to the feature estimator instance that will be used
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* \note the feature estimator instance should already have the input data given beforehand
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* and everything set, ready to be given the compute () command
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*/
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inline void
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setFeatureEstimator (FeatureEstimatorPtr feature_estimator) { feature_estimator_ = feature_estimator; };
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/** \brief Getter method for the feature estimator */
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inline FeatureEstimatorPtr
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getFeatureEstimator () { return feature_estimator_; }
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/** \brief Provide a pointer to the feature representation to use to convert features to k-D vectors.
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* \param feature_representation the const boost shared pointer to a PointRepresentation
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*/
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inline void
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setPointRepresentation (const FeatureRepresentationConstPtr& feature_representation) { feature_representation_ = feature_representation; }
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/** \brief Get a pointer to the feature representation used when converting features into k-D vectors. */
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inline FeatureRepresentationConstPtr const
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getPointRepresentation () { return feature_representation_; }
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/** \brief Sets the alpha parameter
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* \param alpha value to replace the current alpha with
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*/
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inline void
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setAlpha (float alpha) { alpha_ = alpha; }
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/** \brief Get the value of the alpha parameter */
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inline float
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getAlpha () { return alpha_; }
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/** \brief Method for setting the distance metric that will be used for computing the difference between feature vectors
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* \param distance_metric the new distance metric chosen from the NormType enum
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*/
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inline void
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setDistanceMetric (NormType distance_metric) { distance_metric_ = distance_metric; }
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/** \brief Returns the distance metric that is currently used to calculate the difference between feature vectors */
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inline NormType
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getDistanceMetric () { return distance_metric_; }
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private:
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/** \brief Checks if all the necessary input was given and the computations can successfully start */
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bool
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initCompute ();
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/** \brief Method to compute the features for the point cloud at the given scale */
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virtual void
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computeFeatureAtScale (float &scale,
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FeatureCloudPtr &features);
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/** \brief Function that calculates the scalar difference between two features
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* \return the difference as a floating point type
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*/
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float
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distanceBetweenFeatures (const std::vector<float> &a,
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const std::vector<float> &b);
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/** \brief Method that averages all the features at all scales in order to obtain the global mean feature;
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* this value is stored in the mean_feature field
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*/
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void
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calculateMeanFeature ();
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/** \brief Selects the so-called 'unique' features from the cloud of features at each level.
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* These features are the ones that fall outside the standard deviation * alpha_
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*/
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void
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extractUniqueFeatures ();
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/** \brief The general parameter for determining each scale level */
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std::vector<float> scale_values_;
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/** \brief Parameter that determines if a feature is to be considered unique or not */
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float alpha_;
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/** \brief Parameter that determines which distance metric is to be usedto calculate the difference between feature vectors */
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NormType distance_metric_;
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/** \brief the feature estimator that will be used to determine the feature set at each scale level */
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FeatureEstimatorPtr feature_estimator_;
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std::vector<FeatureCloudPtr> features_at_scale_;
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std::vector<std::vector<std::vector<float> > > features_at_scale_vectorized_;
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std::vector<float> mean_feature_;
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FeatureRepresentationConstPtr feature_representation_;
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/** \brief Two structures in which to hold the results of the unique feature extraction process.
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* They are superfluous with respect to each other, but improve the time performance of the algorithm
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*/
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std::vector<std::list<std::size_t> > unique_features_indices_;
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std::vector<std::vector<bool> > unique_features_table_;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/multiscale_feature_persistence.hpp>
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#endif
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