correspondence_estimation_normal_shooting.hpp

/*
 * Software License Agreement (BSD License)
 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2010-2012, Willow Garage, Inc.
 *  Copyright (c) 2012-, Open Perception, 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 the copyright holder(s) 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.
 *
 * $Id$
 *
 */
 
#ifndef PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_
#define PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_
 
#include <pcl/common/copy_point.h>
 
namespace pcl {
 
namespace registration {
 
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
bool
CorrespondenceEstimationNormalShooting<PointSource, PointTarget, NormalT, Scalar>::
    initCompute()
{
  if (!source_normals_) {
    PCL_WARN("[pcl::registration::%s::initCompute] Datasets containing normals for "
             "source have not been given!\n",
             getClassName().c_str());
    return (false);
  }
 
  return (
      CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::initCompute());
}
 
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
void
CorrespondenceEstimationNormalShooting<PointSource, PointTarget, NormalT, Scalar>::
    determineCorrespondences(pcl::Correspondences& correspondences,
                             const double max_distance)
{
  if (!initCompute())
    return;
 
  correspondences.resize(indices_->size());
 
  pcl::Indices nn_indices(k_);
  std::vector<float> nn_dists(k_);
 
  int min_index = 0;
 
  pcl::Correspondence corr;
  unsigned int nr_valid_correspondences = 0;
 
  PointTarget pt;
  // Iterate over the input set of source indices
  for (const auto& idx_i : (*indices_)) {
    // Check if the template types are the same. If true, avoid a copy.
    // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
    // macro!
    tree_->nearestKSearch(
        detail::pointCopyOrRef<PointTarget, PointSource>(input_, idx_i),
        k_,
        nn_indices,
        nn_dists);
 
    // Among the K nearest neighbours find the one with minimum perpendicular distance
    // to the normal
    double min_dist = std::numeric_limits<double>::max();
 
    // Find the best correspondence
    for (std::size_t j = 0; j < nn_indices.size(); j++) {
      // computing the distance between a point and a line in 3d.
      // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
      pt.x = (*target_)[nn_indices[j]].x - (*input_)[idx_i].x;
      pt.y = (*target_)[nn_indices[j]].y - (*input_)[idx_i].y;
      pt.z = (*target_)[nn_indices[j]].z - (*input_)[idx_i].z;
 
      const NormalT& normal = (*source_normals_)[idx_i];
      Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
      Eigen::Vector3d V(pt.x, pt.y, pt.z);
      Eigen::Vector3d C = N.cross(V);
 
      // Check if we have a better correspondence
      const double dist = C.dot(C);
      if (dist < min_dist) {
        min_dist = dist;
        min_index = static_cast<int>(j);
      }
    }
    if (min_dist > max_distance)
      continue;
 
    corr.index_query = idx_i;
    corr.index_match = nn_indices[min_index];
    corr.distance = nn_dists[min_index]; // min_dist;
    correspondences[nr_valid_correspondences++] = corr;
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}
 
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
void
CorrespondenceEstimationNormalShooting<PointSource, PointTarget, NormalT, Scalar>::
    determineReciprocalCorrespondences(pcl::Correspondences& correspondences,
                                       const double max_distance)
{
  if (!initCompute())
    return;
 
  // setup tree for reciprocal search
  // Set the internal point representation of choice
  if (!initComputeReciprocal())
    return;
 
  correspondences.resize(indices_->size());
 
  pcl::Indices nn_indices(k_);
  std::vector<float> nn_dists(k_);
  pcl::Indices index_reciprocal(1);
  std::vector<float> distance_reciprocal(1);
 
  int min_index = 0;
 
  pcl::Correspondence corr;
  unsigned int nr_valid_correspondences = 0;
  int target_idx = 0;
 
  PointTarget pt;
  // Iterate over the input set of source indices
  for (const auto& idx_i : (*indices_)) {
    // Check if the template types are the same. If true, avoid a copy.
    // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
    // macro!
    tree_->nearestKSearch(
        detail::pointCopyOrRef<PointTarget, PointSource>(input_, idx_i),
        k_,
        nn_indices,
        nn_dists);
 
    // Among the K nearest neighbours find the one with minimum perpendicular distance
    // to the normal
    double min_dist = std::numeric_limits<double>::max();
 
    // Find the best correspondence
    for (std::size_t j = 0; j < nn_indices.size(); j++) {
      // computing the distance between a point and a line in 3d.
      // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
      pt.x = (*target_)[nn_indices[j]].x - (*input_)[idx_i].x;
      pt.y = (*target_)[nn_indices[j]].y - (*input_)[idx_i].y;
      pt.z = (*target_)[nn_indices[j]].z - (*input_)[idx_i].z;
 
      const NormalT& normal = (*source_normals_)[idx_i];
      Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
      Eigen::Vector3d V(pt.x, pt.y, pt.z);
      Eigen::Vector3d C = N.cross(V);
 
      // Check if we have a better correspondence
      const double dist = C.dot(C);
      if (dist < min_dist) {
        min_dist = dist;
        min_index = static_cast<int>(j);
      }
    }
    if (min_dist > max_distance)
      continue;
 
    // Check if the correspondence is reciprocal
    target_idx = nn_indices[min_index];
    tree_reciprocal_->nearestKSearch(
        detail::pointCopyOrRef<PointSource, PointTarget>(target_, target_idx),
        1,
        index_reciprocal,
        distance_reciprocal);
 
    if (idx_i != index_reciprocal[0])
      continue;
 
    // Correspondence IS reciprocal, save it and continue
    corr.index_query = idx_i;
    corr.index_match = nn_indices[min_index];
    corr.distance = nn_dists[min_index]; // min_dist;
    correspondences[nr_valid_correspondences++] = corr;
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}
 
} // namespace registration
} // namespace pcl
 
#endif // PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_