350 lines
15 KiB
C
350 lines
15 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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#include <pcl/memory.h>
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#include <pcl/pcl_macros.h>
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#include <pcl/pcl_base.h>
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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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#include <pcl/common/eigen.h>
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#include <pcl/common/centroid.h>
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#include <map>
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#include <numeric> // for std::iota
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namespace pcl
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{
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/** \brief SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection.
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* \author Radu Bogdan Rusu
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* \ingroup sample_consensus
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*/
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template <typename PointT>
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class SampleConsensusModelRegistration : 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>::error_sqr_dists_;
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using SampleConsensusModel<PointT>::isModelValid;
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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<SampleConsensusModelRegistration<PointT> >;
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using ConstPtr = shared_ptr<const SampleConsensusModelRegistration<PointT>>;
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/** \brief Constructor for base SampleConsensusModelRegistration.
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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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SampleConsensusModelRegistration (const PointCloudConstPtr &cloud,
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bool random = false)
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: SampleConsensusModel<PointT> (cloud, random)
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, target_ ()
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, sample_dist_thresh_ (0)
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{
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// Call our own setInputCloud
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setInputCloud (cloud);
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model_name_ = "SampleConsensusModelRegistration";
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sample_size_ = 3;
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model_size_ = 16;
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}
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/** \brief Constructor for base SampleConsensusModelRegistration.
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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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SampleConsensusModelRegistration (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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, target_ ()
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, sample_dist_thresh_ (0)
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{
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computeOriginalIndexMapping ();
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computeSampleDistanceThreshold (cloud, indices);
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model_name_ = "SampleConsensusModelRegistration";
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sample_size_ = 3;
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model_size_ = 16;
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}
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/** \brief Empty destructor */
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~SampleConsensusModelRegistration () {}
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/** \brief Provide a pointer to the input dataset
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* \param[in] cloud the const boost shared pointer to a PointCloud message
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*/
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inline void
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setInputCloud (const PointCloudConstPtr &cloud) override
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{
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SampleConsensusModel<PointT>::setInputCloud (cloud);
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computeOriginalIndexMapping ();
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computeSampleDistanceThreshold (cloud);
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}
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/** \brief Set the input point cloud target.
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* \param[in] target the input point cloud target
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*/
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inline void
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setInputTarget (const PointCloudConstPtr &target)
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{
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target_ = target;
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// Cache the size and fill the target indices
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const index_t target_size = static_cast<index_t> (target->size ());
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indices_tgt_.reset (new Indices (target_size));
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std::iota (indices_tgt_->begin (), indices_tgt_->end (), 0);
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computeOriginalIndexMapping ();
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}
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/** \brief Set the input point cloud target.
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* \param[in] target the input point cloud target
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* \param[in] indices_tgt a vector of point indices to be used from \a target
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*/
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inline void
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setInputTarget (const PointCloudConstPtr &target, const Indices &indices_tgt)
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{
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target_ = target;
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indices_tgt_.reset (new Indices (indices_tgt));
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computeOriginalIndexMapping ();
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}
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/** \brief Compute a 4x4 rigid transformation matrix from the samples given
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* \param[in] samples the indices found as 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 transformed points to their correspondences
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* \param[in] model_coefficients the 4x4 transformation matrix
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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 4x4 transformation matrix
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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 4x4 transformation using the given inlier set
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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 transformation
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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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void
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projectPoints (const Indices &,
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const Eigen::VectorXf &,
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PointCloud &, bool = true) const override
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{
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};
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bool
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doSamplesVerifyModel (const std::set<index_t> &,
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const Eigen::VectorXf &,
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const double) const override
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{
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return (false);
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}
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/** \brief Return a unique id for this model (SACMODEL_REGISTRATION). */
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inline pcl::SacModel
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getModelType () const override { return (SACMODEL_REGISTRATION); }
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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 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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/** \brief Computes an "optimal" sample distance threshold based on the
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* principal directions of the input cloud.
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* \param[in] cloud the const boost shared pointer to a PointCloud message
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*/
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inline void
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computeSampleDistanceThreshold (const PointCloudConstPtr &cloud)
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{
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// Compute the principal directions via PCA
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Eigen::Vector4f xyz_centroid;
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Eigen::Matrix3f covariance_matrix = Eigen::Matrix3f::Zero ();
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computeMeanAndCovarianceMatrix (*cloud, covariance_matrix, xyz_centroid);
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// Check if the covariance matrix is finite or not.
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for (int i = 0; i < 3; ++i)
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for (int j = 0; j < 3; ++j)
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if (!std::isfinite (covariance_matrix.coeffRef (i, j)))
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PCL_ERROR ("[pcl::SampleConsensusModelRegistration::computeSampleDistanceThreshold] Covariance matrix has NaN values! Is the input cloud finite?\n");
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Eigen::Vector3f eigen_values;
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pcl::eigen33 (covariance_matrix, eigen_values);
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// Compute the distance threshold for sample selection
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sample_dist_thresh_ = eigen_values.array ().sqrt ().sum () / 3.0;
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sample_dist_thresh_ *= sample_dist_thresh_;
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PCL_DEBUG ("[pcl::SampleConsensusModelRegistration::setInputCloud] Estimated a sample selection distance threshold of: %f\n", sample_dist_thresh_);
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}
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/** \brief Computes an "optimal" sample distance threshold based on the
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* principal directions of the input cloud.
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* \param[in] cloud the const boost shared pointer to a PointCloud message
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* \param indices
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*/
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inline void
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computeSampleDistanceThreshold (const PointCloudConstPtr &cloud,
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const Indices &indices)
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{
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// Compute the principal directions via PCA
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Eigen::Vector4f xyz_centroid;
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Eigen::Matrix3f covariance_matrix;
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computeMeanAndCovarianceMatrix (*cloud, indices, covariance_matrix, xyz_centroid);
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// Check if the covariance matrix is finite or not.
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for (int i = 0; i < 3; ++i)
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for (int j = 0; j < 3; ++j)
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if (!std::isfinite (covariance_matrix.coeffRef (i, j)))
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PCL_ERROR ("[pcl::SampleConsensusModelRegistration::computeSampleDistanceThreshold] Covariance matrix has NaN values! Is the input cloud finite?\n");
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Eigen::Vector3f eigen_values;
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pcl::eigen33 (covariance_matrix, eigen_values);
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// Compute the distance threshold for sample selection
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sample_dist_thresh_ = eigen_values.array ().sqrt ().sum () / 3.0;
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sample_dist_thresh_ *= sample_dist_thresh_;
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PCL_DEBUG ("[pcl::SampleConsensusModelRegistration::setInputCloud] Estimated a sample selection distance threshold of: %f\n", sample_dist_thresh_);
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}
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/** \brief Estimate a rigid transformation between a source and a target point cloud using an SVD closed-form
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* solution of absolute orientation using unit quaternions
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* \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 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 interest points from
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* indices_src
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* \param[out] transform the resultant transformation matrix (as model coefficients)
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*
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* This method is an implementation of: Horn, B. “Closed-Form Solution of Absolute Orientation Using Unit Quaternions,” JOSA A, Vol. 4, No. 4, 1987
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*/
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void
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estimateRigidTransformationSVD (const pcl::PointCloud<PointT> &cloud_src,
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const Indices &indices_src,
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const pcl::PointCloud<PointT> &cloud_tgt,
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const Indices &indices_tgt,
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Eigen::VectorXf &transform) const;
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/** \brief Compute mappings between original indices of the input_/target_ clouds. */
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void
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computeOriginalIndexMapping ()
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{
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if (!indices_tgt_)
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{
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PCL_DEBUG ("[pcl::SampleConsensusModelRegistration::computeOriginalIndexMapping] Cannot compute mapping: indices_tgt_ is null.\n");
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return;
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}
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if (!indices_)
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{
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PCL_DEBUG ("[pcl::SampleConsensusModelRegistration::computeOriginalIndexMapping] Cannot compute mapping: indices_ is null.\n");
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return;
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}
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if (indices_->empty ())
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{
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PCL_DEBUG ("[pcl::SampleConsensusModelRegistration::computeOriginalIndexMapping] Cannot compute mapping: indices_ is empty.\n");
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return;
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}
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if (indices_->size () != indices_tgt_->size ())
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{
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PCL_DEBUG ("[pcl::SampleConsensusModelRegistration::computeOriginalIndexMapping] Cannot compute mapping: indices_ and indices_tgt_ are not the same size (%zu vs %zu).\n",
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indices_->size (), indices_tgt_->size ());
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return;
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}
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for (std::size_t i = 0; i < indices_->size (); ++i)
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correspondences_[(*indices_)[i]] = (*indices_tgt_)[i];
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PCL_DEBUG ("[pcl::SampleConsensusModelRegistration::computeOriginalIndexMapping] Successfully computed mapping.\n");
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}
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/** \brief A boost shared pointer to the target point cloud data array. */
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PointCloudConstPtr target_;
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/** \brief A pointer to the vector of target point indices to use. */
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IndicesPtr indices_tgt_;
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/** \brief Given the index in the original point cloud, give the matching original index in the target cloud */
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std::map<index_t, index_t> correspondences_;
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/** \brief Internal distance threshold used for the sample selection step. */
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double sample_dist_thresh_;
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public:
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PCL_MAKE_ALIGNED_OPERATOR_NEW
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};
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}
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#include <pcl/sample_consensus/impl/sac_model_registration.hpp>
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