correspondence_estimation.h

Go to the documentation of this file.

/*
 * Software License Agreement (BSD License)
 *
 *  Copyright (c) 2010, Willow Garage, Inc.
 *  All rights reserved.
 *
 *  Redistribution and use in source and binary forms, with or without
 *  modification, are permitted provided that the following conditions
 *  are met:
 *
 *   * Redistributions of source code must retain the above copyright
 *     notice, this list of conditions and the following disclaimer.
 *   * Redistributions in binary form must reproduce the above
 *     copyright notice, this list of conditions and the following
 *     disclaimer in the documentation and/or other materials provided
 *     with the distribution.
 *   * Neither the name of Willow Garage, Inc. nor the names of its
 *     contributors may be used to endorse or promote products derived
 *     from this software without specific prior written permission.
 *
 *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 *  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
 *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
 *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
 *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
 *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 *  POSSIBILITY OF SUCH DAMAGE.
 *
 *
 */

#ifndef PCL_REGISTRATION_CORRESPONDENCE_ESTIMATION_H_
#define PCL_REGISTRATION_CORRESPONDENCE_ESTIMATION_H_

#include <map>
#include <string>
#include <boost/function.hpp>
#include <boost/bind.hpp>

#include <pcl/pcl_base.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/kdtree/kdtree_flann.h>
#include "pcl/win32_macros.h"

#include <pcl/registration/correspondence_types.h>



namespace pcl
{
  namespace registration
  {
    template <typename PointSource, typename PointTarget>
    class CorrespondenceEstimation : public PCLBase<PointSource>
    {

      class FeatureContainerInterface;

    public:
      using PCLBase<PointSource>::initCompute;
      using PCLBase<PointSource>::deinitCompute;
      using PCLBase<PointSource>::input_;
      using PCLBase<PointSource>::indices_;

      typedef typename pcl::KdTree<PointTarget> KdTree;
      typedef typename pcl::KdTree<PointTarget>::Ptr KdTreePtr;

      typedef pcl::PointCloud<PointSource> PointCloudSource;
      typedef typename PointCloudSource::Ptr PointCloudSourcePtr;
      typedef typename PointCloudSource::ConstPtr PointCloudSourceConstPtr;

      typedef pcl::PointCloud<PointTarget> PointCloudTarget;
      typedef typename PointCloudTarget::Ptr PointCloudTargetPtr;
      typedef typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr;

      typedef typename KdTree::PointRepresentationConstPtr PointRepresentationConstPtr;

      typedef std::map<std::string, boost::shared_ptr<FeatureContainerInterface> > FeaturesMap;


      CorrespondenceEstimation () : target_ (),
          corr_dist_threshold_ (std::numeric_limits<double>::max()),
          point_representation_ ()
      {
        tree_ = boost::make_shared<pcl::KdTreeFLANN<PointTarget> > ();     // FLANN tree for nearest neighbor search
      }

      virtual inline void setInputTarget (const PointCloudTargetConstPtr &cloud);

      inline PointCloudTargetConstPtr const getInputTarget () { return (target_ ); }

      template <typename FeatureType>
      inline void setSourceFeature (const typename pcl::PointCloud<FeatureType>::ConstPtr &source_feature, std::string key);

      template <typename FeatureType>
      inline typename pcl::PointCloud<FeatureType>::ConstPtr getSourceFeature (std::string key);

      template <typename FeatureType>
      inline void setTargetFeature (const typename pcl::PointCloud<FeatureType>::ConstPtr &target_feature, std::string key);

      template <typename FeatureType>
      inline typename pcl::PointCloud<FeatureType>::ConstPtr getTargetFeature (std::string key);

      template <typename FeatureType>
      inline void setRadiusSearch (const typename pcl::KdTree<FeatureType>::Ptr &tree, float r, std::string key);

      template <typename FeatureType>
      inline void setKSearch (const typename pcl::KdTree<FeatureType>::Ptr &tree, int k, std::string key);

      inline void setMaxCorrespondenceDistance (double distance_threshold) { corr_dist_threshold_ = distance_threshold; }

      inline double getMaxCorrespondenceDistance () { return (corr_dist_threshold_); }

      inline void
        setPointRepresentation (const PointRepresentationConstPtr &point_representation)
      {
        point_representation_ = point_representation;
      }

      inline bool
      searchForNeighbors (const PointCloudSource &cloud, int index, std::vector<int> &indices, std::vector<float> &distances)
      {
        int k = tree_->nearestKSearch (cloud, index, 1, indices, distances);
        if (k == 0)
          return (false);
        return (true);
      }

//      /** brief Deterime the correspondences for the points in a given cloud (closest nearest neighbor).
//        * param cloud the point cloud dataset to use for nearest neighbor search
//        * param correspondences the found correspondences (index of query point, index of target point, distance)
//        */
//      inline void
//      determineCorrespondences(const PointCloudSource &cloud, std::vector<pcl::registration::Correspondence> &correspondences)
//      {
//        if (cloud.points.size() == 0)
//          return;
//
//        correspondences.resize(cloud.points.size());
//        std::vector<int> index(1);
//        std::vector<float> distance(1);
//        pcl::registration::Correspondence corr;
//        for (unsigned int i = 0; i < cloud.points.size(); ++i)
//        {
//          if ( tree_->nearestKSearch(cloud, i, 1, index, distance) )
//          {
//            corr.indexQuery = i;
//            corr.indexMatch = index[0];
//            corr.distance = distance[0];
//            correspondences[i] = corr;
//          }
//          else
//          {
//            correspondences[i] = pcl::registration::Correspondence(i, -1, std::numeric_limits<float>::max());
//          }
//        }
//      }

//      /** brief Deterime the correspondences for the points in a given cloud (closest nearest neighbor).
//        * param cloud the point cloud dataset to use for nearest neighbor search
//        * param indices indices of the points for which correspondences should be determined
//        * param correspondences the found correspondences (index of query point, index of target point, distance)
//        */
//      inline void
//      determineCorrespondences(const PointCloudSource &cloud, const std::vector<int>& indices, std::vector<pcl::registration::Correspondence> &correspondences)
//      {
//        if (cloud.points.size() == 0)
//          return;
//
//        correspondences.resize(cloud.points.size());
//        std::vector<int> index(1);
//        std::vector<float> distance(1);
//        pcl::registration::Correspondence corr;
//        for (unsigned int i = 0; i < indices.size(); ++i)
//        {
//          if ( tree_->nearestKSearch(cloud, indices[i], 1, index, distance) )
//          {
//            corr.indexQuery = i;
//            corr.indexMatch = index[0];
//            corr.distance = distance[0];
//            correspondences[i] = corr;
//          }
//          else
//          {
//            correspondences[i] = pcl::registration::Correspondence(i, -1, std::numeric_limits<float>::max());
//          }
//        }
//      }


      virtual void determineCorrespondences (std::vector<pcl::registration::Correspondence> &correspondences, float max_distance = std::numeric_limits<float>::max());

      virtual void determineReciprocalCorrespondences(std::vector<pcl::registration::Correspondence> &correspondences);


    protected:
      std::string corr_name_;

      KdTreePtr tree_;

      PointCloudTargetConstPtr target_;

      double corr_dist_threshold_;

      inline bool hasValidFeatures ();

      inline void findFeatureCorrespondences (int index, std::vector<int> &correspondence_indices);

      inline const std::string& getClassName () const { return (corr_name_); }

    private:

      PointRepresentationConstPtr point_representation_;

      FeaturesMap features_map_;

      template <typename FeatureType>
      class FeatureContainer : public pcl::registration::CorrespondenceEstimation<PointSource, PointTarget>::FeatureContainerInterface
      {
        public:
          typedef typename pcl::PointCloud<FeatureType>::ConstPtr FeatureCloudConstPtr;
          typedef typename pcl::KdTree<FeatureType> KdTree;
          typedef typename pcl::KdTree<FeatureType>::Ptr KdTreePtr;
          typedef boost::function<bool (const pcl::PointCloud<FeatureType> &, int, std::vector<int> &,
                                        std::vector<float> &)> SearchMethod;

          FeatureContainer () : k_(0), radius_(0) {}

          void setSourceFeature (const FeatureCloudConstPtr &source_features)
          {
            source_features_ = source_features;
          }

          FeatureCloudConstPtr getSourceFeature ()
          {
            return (source_features_);
          }

          void setTargetFeature (const FeatureCloudConstPtr &target_features)
          {
            target_features_ = target_features;
            if (tree_)
            {
              tree_->setInputCloud (target_features_);
            }
          }

          FeatureCloudConstPtr getTargetFeature ()
          {
            return (target_features_);
          }

          void setRadiusSearch (KdTreePtr tree, float r)
          {
            tree_ = tree;
            radius_ = r;
            k_ = 0;
            if (target_features_)
            {
              tree_->setInputCloud (target_features_);
            }
          }

          void setKSearch (KdTreePtr tree, int k)
          {
            tree_ = tree;
            k_ = k;
            radius_ = 0.0;
            if (target_features_)
            {
              tree_->setInputCloud (target_features_);
            }
          }

          virtual bool isValid ()
          {
            if (!source_features_ || !target_features_ || !tree_)
            {
              return (false);
            }
            else
            {
              return (source_features_->points.size () > 0 &&
                      target_features_->points.size () > 0 &&
                      (k_ > 0 || radius_ > 0.0));
            }
          }

          virtual void findFeatureCorrespondences (int index, std::vector<int> &correspondence_indices,
                                                   std::vector<float> &distances)
          {
            if (k_ > 0)
            {
              correspondence_indices.resize (k_);
              distances.resize (k_);
              tree_->nearestKSearch (*source_features_, index, k_, correspondence_indices, distances);
            }
            else
            {
              tree_->radiusSearch (*source_features_, index, radius_, correspondence_indices, distances);
            }
          }

        private:
          FeatureCloudConstPtr source_features_, target_features_;
          KdTreePtr tree_;
          SearchMethod search_method_;
          int k_;
          double radius_;
      };

      class FeatureContainerInterface
      {
        public:
          virtual bool isValid () = 0;
          virtual void findFeatureCorrespondences (int index, std::vector<int> &correspondence_indices,
                                                   std::vector<float> &distances) = 0;
      };

   };


  }
}

#include "pcl/registration/impl/correspondence_estimation.hpp"

#endif /* PCL_REGISTRATION_CORRESPONDENCE_ESTIMATION_H_ */

---

pcl
Author(s): See http://pcl.ros.org/authors for the complete list of authors.
autogenerated on Fri Jan 11 09:55:21 2013