313 lines
13 KiB
C
313 lines
13 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) 2010-2011, 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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#ifdef __SSE__
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#include <xmmintrin.h> // for __m128
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#endif // ifdef __SSE__
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#ifdef __AVX__
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#include <immintrin.h> // for __m256
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#endif // ifdef __AVX__
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#include <pcl/sample_consensus/sac_model.h>
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#include <pcl/sample_consensus/model_types.h>
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namespace pcl
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{
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/** \brief SampleConsensusModelSphere defines a model for 3D sphere segmentation.
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* The model coefficients are defined as:
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* - \b center.x : the X coordinate of the sphere's center
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* - \b center.y : the Y coordinate of the sphere's center
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* - \b center.z : the Z coordinate of the sphere's center
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* - \b radius : the sphere's radius
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*
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* \author Radu B. Rusu
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* \ingroup sample_consensus
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*/
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template <typename PointT>
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class SampleConsensusModelSphere : public SampleConsensusModel<PointT>
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{
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public:
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using SampleConsensusModel<PointT>::model_name_;
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using SampleConsensusModel<PointT>::input_;
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using SampleConsensusModel<PointT>::indices_;
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using SampleConsensusModel<PointT>::radius_min_;
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using SampleConsensusModel<PointT>::radius_max_;
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using SampleConsensusModel<PointT>::error_sqr_dists_;
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using PointCloud = typename SampleConsensusModel<PointT>::PointCloud;
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using PointCloudPtr = typename SampleConsensusModel<PointT>::PointCloudPtr;
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using PointCloudConstPtr = typename SampleConsensusModel<PointT>::PointCloudConstPtr;
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using Ptr = shared_ptr<SampleConsensusModelSphere<PointT> >;
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using ConstPtr = shared_ptr<const SampleConsensusModelSphere<PointT>>;
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/** \brief Constructor for base SampleConsensusModelSphere.
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* \param[in] cloud the input point cloud dataset
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* \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
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*/
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SampleConsensusModelSphere (const PointCloudConstPtr &cloud,
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bool random = false)
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: SampleConsensusModel<PointT> (cloud, random)
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{
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model_name_ = "SampleConsensusModelSphere";
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sample_size_ = 4;
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model_size_ = 4;
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}
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/** \brief Constructor for base SampleConsensusModelSphere.
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* \param[in] cloud the input point cloud dataset
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* \param[in] indices a vector of point indices to be used from \a cloud
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* \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
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*/
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SampleConsensusModelSphere (const PointCloudConstPtr &cloud,
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const Indices &indices,
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bool random = false)
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: SampleConsensusModel<PointT> (cloud, indices, random)
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{
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model_name_ = "SampleConsensusModelSphere";
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sample_size_ = 4;
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model_size_ = 4;
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}
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/** \brief Empty destructor */
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~SampleConsensusModelSphere () {}
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/** \brief Copy constructor.
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* \param[in] source the model to copy into this
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*/
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SampleConsensusModelSphere (const SampleConsensusModelSphere &source) :
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SampleConsensusModel<PointT> ()
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{
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*this = source;
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model_name_ = "SampleConsensusModelSphere";
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}
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/** \brief Copy constructor.
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* \param[in] source the model to copy into this
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*/
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inline SampleConsensusModelSphere&
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operator = (const SampleConsensusModelSphere &source)
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{
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SampleConsensusModel<PointT>::operator=(source);
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return (*this);
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}
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/** \brief Check whether the given index samples can form a valid sphere model, compute the model
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* coefficients from these samples and store them internally in model_coefficients.
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* The sphere coefficients are: x, y, z, R.
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* \param[in] samples the point indices found as possible good candidates for creating a valid model
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* \param[out] model_coefficients the resultant model coefficients
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*/
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bool
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computeModelCoefficients (const Indices &samples,
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Eigen::VectorXf &model_coefficients) const override;
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/** \brief Compute all distances from the cloud data to a given sphere model.
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* \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
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* \param[out] distances the resultant estimated distances
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*/
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void
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getDistancesToModel (const Eigen::VectorXf &model_coefficients,
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std::vector<double> &distances) const override;
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/** \brief Select all the points which respect the given model coefficients as inliers.
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* \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
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* \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
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* \param[out] inliers the resultant model inliers
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*/
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void
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selectWithinDistance (const Eigen::VectorXf &model_coefficients,
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const double threshold,
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Indices &inliers) override;
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/** \brief Count all the points which respect the given model coefficients as inliers.
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*
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* \param[in] model_coefficients the coefficients of a model that we need to compute distances to
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* \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
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* \return the resultant number of inliers
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*/
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std::size_t
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countWithinDistance (const Eigen::VectorXf &model_coefficients,
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const double threshold) const override;
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/** \brief Recompute the sphere coefficients using the given inlier set and return them to the user.
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* @note: these are the coefficients of the sphere model after refinement (e.g. after SVD)
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* \param[in] inliers the data inliers found as supporting the model
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* \param[in] model_coefficients the initial guess for the optimization
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* \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
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*/
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void
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optimizeModelCoefficients (const Indices &inliers,
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const Eigen::VectorXf &model_coefficients,
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Eigen::VectorXf &optimized_coefficients) const override;
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/** \brief Create a new point cloud with inliers projected onto the sphere model.
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* \param[in] inliers the data inliers that we want to project on the sphere model
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* \param[in] model_coefficients the coefficients of a sphere model
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* \param[out] projected_points the resultant projected points
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* \param[in] copy_data_fields set to true if we need to copy the other data fields
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* \todo implement this.
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*/
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void
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projectPoints (const Indices &inliers,
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const Eigen::VectorXf &model_coefficients,
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PointCloud &projected_points,
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bool copy_data_fields = true) const override;
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/** \brief Verify whether a subset of indices verifies the given sphere model coefficients.
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* \param[in] indices the data indices that need to be tested against the sphere model
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* \param[in] model_coefficients the sphere model coefficients
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* \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
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*/
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bool
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doSamplesVerifyModel (const std::set<index_t> &indices,
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const Eigen::VectorXf &model_coefficients,
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const double threshold) const override;
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/** \brief Return a unique id for this model (SACMODEL_SPHERE). */
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inline pcl::SacModel getModelType () const override { return (SACMODEL_SPHERE); }
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protected:
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using SampleConsensusModel<PointT>::sample_size_;
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using SampleConsensusModel<PointT>::model_size_;
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/** \brief Check whether a model is valid given the user constraints.
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* \param[in] model_coefficients the set of model coefficients
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*/
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bool
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isModelValid (const Eigen::VectorXf &model_coefficients) const override
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{
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if (!SampleConsensusModel<PointT>::isModelValid (model_coefficients))
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return (false);
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if (radius_min_ != -std::numeric_limits<double>::max() && model_coefficients[3] < radius_min_)
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return (false);
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if (radius_max_ != std::numeric_limits<double>::max() && model_coefficients[3] > radius_max_)
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return (false);
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return (true);
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}
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/** \brief Check if a sample of indices results in a good sample of points
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* indices.
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* \param[in] samples the resultant index samples
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*/
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bool
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isSampleGood(const Indices &samples) const override;
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/** This implementation uses no SIMD instructions. It is not intended for normal use.
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* See countWithinDistance which automatically uses the fastest implementation.
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*/
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std::size_t
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countWithinDistanceStandard (const Eigen::VectorXf &model_coefficients,
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const double threshold,
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std::size_t i = 0) const;
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#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
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/** This implementation uses SSE, SSE2, and SSE4.1 instructions. It is not intended for normal use.
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* See countWithinDistance which automatically uses the fastest implementation.
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*/
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std::size_t
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countWithinDistanceSSE (const Eigen::VectorXf &model_coefficients,
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const double threshold,
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std::size_t i = 0) const;
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#endif
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#if defined (__AVX__) && defined (__AVX2__)
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/** This implementation uses AVX and AVX2 instructions. It is not intended for normal use.
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* See countWithinDistance which automatically uses the fastest implementation.
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*/
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std::size_t
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countWithinDistanceAVX (const Eigen::VectorXf &model_coefficients,
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const double threshold,
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std::size_t i = 0) const;
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#endif
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private:
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struct OptimizationFunctor : pcl::Functor<float>
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{
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/** Functor constructor
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* \param[in] indices the indices of data points to evaluate
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* \param[in] estimator pointer to the estimator object
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*/
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OptimizationFunctor (const pcl::SampleConsensusModelSphere<PointT> *model, const Indices& indices) :
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pcl::Functor<float> (indices.size ()), model_ (model), indices_ (indices) {}
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/** Cost function to be minimized
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* \param[in] x the variables array
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* \param[out] fvec the resultant functions evaluations
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* \return 0
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*/
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int
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operator() (const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
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{
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Eigen::Vector4f cen_t;
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cen_t[3] = 0;
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for (int i = 0; i < values (); ++i)
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{
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// Compute the difference between the center of the sphere and the datapoint X_i
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cen_t.head<3>() = (*model_->input_)[indices_[i]].getVector3fMap() - x.head<3>();
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// g = sqrt ((x-a)^2 + (y-b)^2 + (z-c)^2) - R
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fvec[i] = std::sqrt (cen_t.dot (cen_t)) - x[3];
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}
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return (0);
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}
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const pcl::SampleConsensusModelSphere<PointT> *model_;
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const Indices &indices_;
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};
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#ifdef __AVX__
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inline __m256 sqr_dist8 (const std::size_t i, const __m256 a_vec, const __m256 b_vec, const __m256 c_vec) const;
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#endif
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#ifdef __SSE__
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inline __m128 sqr_dist4 (const std::size_t i, const __m128 a_vec, const __m128 b_vec, const __m128 c_vec) const;
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#endif
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/sample_consensus/impl/sac_model_sphere.hpp>
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#endif
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