registration.hpp

/*
 * Software License Agreement (BSD License)
 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2010-2011, 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.
 *
 * $Id$
 *
 */
 
#pragma once
 
#include <pcl/search/auto.h>
 
namespace pcl {
 
template <typename PointSource, typename PointTarget, typename Scalar>
inline void
Registration<PointSource, PointTarget, Scalar>::setInputSource(
    const PointCloudSourceConstPtr& cloud)
{
  if (cloud->points.empty()) {
    PCL_ERROR("[pcl::%s::setInputSource] Invalid or empty point cloud dataset given!\n",
              getClassName().c_str());
    return;
  }
  source_cloud_updated_ = true;
  PCLBase<PointSource>::setInputCloud(cloud);
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
inline void
Registration<PointSource, PointTarget, Scalar>::setInputTarget(
    const PointCloudTargetConstPtr& cloud)
{
  if (cloud->points.empty()) {
    PCL_ERROR("[pcl::%s::setInputTarget] Invalid or empty point cloud dataset given!\n",
              getClassName().c_str());
    return;
  }
  target_ = cloud;
  target_cloud_updated_ = true;
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
bool
Registration<PointSource, PointTarget, Scalar>::initCompute()
{
  if (!target_) {
    PCL_ERROR("[pcl::registration::%s::compute] No input target dataset was given!\n",
              getClassName().c_str());
    return (false);
  }
 
  // Only update target kd-tree if a new target cloud was set
  if (target_cloud_updated_ && !force_no_recompute_) {
    if (point_representation_) {
      if (!tree_ || !tree_->setPointRepresentation(point_representation_) ||
          !tree_->setInputCloud(target_))
        tree_.reset(pcl::search::autoSelectMethod<PointTarget>(
            target_,
            pcl::IndicesConstPtr(),
            point_representation_,
            false,
            pcl::search::Purpose::one_knn_search));
    }
    else if (!tree_ || !tree_->setInputCloud(target_))
      tree_.reset(pcl::search::autoSelectMethod<PointTarget>(
          target_, false, pcl::search::Purpose::one_knn_search));
    target_cloud_updated_ = false;
  }
 
  // Update the correspondence estimation
  if (correspondence_estimation_) {
    correspondence_estimation_->setSearchMethodTarget(tree_, force_no_recompute_);
    correspondence_estimation_->setSearchMethodSource(tree_reciprocal_,
                                                      force_no_recompute_reciprocal_);
  }
 
  // Note: we /cannot/ update the search method on all correspondence rejectors, because
  // we know nothing about them. If they should be cached, they must be cached
  // individually.
 
  return (PCLBase<PointSource>::initCompute());
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
bool
Registration<PointSource, PointTarget, Scalar>::initComputeReciprocal()
{
  if (!input_) {
    PCL_ERROR("[pcl::registration::%s::compute] No input source dataset was given!\n",
              getClassName().c_str());
    return (false);
  }
 
  if (source_cloud_updated_ && !force_no_recompute_reciprocal_) {
    if (!tree_reciprocal_ || !tree_reciprocal_->setInputCloud(input_))
      tree_reciprocal_.reset(pcl::search::autoSelectMethod<PointSource>(input_, false));
    source_cloud_updated_ = false;
  }
  return (true);
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
inline double
Registration<PointSource, PointTarget, Scalar>::getFitnessScore(
    const std::vector<float>& distances_a, const std::vector<float>& distances_b)
{
  unsigned int nr_elem =
      static_cast<unsigned int>(std::min(distances_a.size(), distances_b.size()));
  Eigen::VectorXf map_a = Eigen::VectorXf::Map(distances_a.data(), nr_elem);
  Eigen::VectorXf map_b = Eigen::VectorXf::Map(distances_b.data(), nr_elem);
  return (static_cast<double>((map_a - map_b).sum()) / static_cast<double>(nr_elem));
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
inline double
Registration<PointSource, PointTarget, Scalar>::getFitnessScore(double max_range,
                                                                bool use_indices)
{
  double fitness_score = 0.0;
 
  // Transform the input dataset using the final transformation
  PointCloudSource input_transformed;
  if (use_indices && indices_ && indices_->size() != input_->size())
    transformPointCloud(*input_, *indices_, input_transformed, final_transformation_);
  else
    transformPointCloud(*input_, input_transformed, final_transformation_);
 
  pcl::Indices nn_indices(1);
  std::vector<float> nn_dists(1);
 
  // For each point in the source dataset
  int nr = 0;
  for (const auto& point : input_transformed) {
    if (!input_->is_dense && !pcl::isXYZFinite(point))
      continue;
    // Find its nearest neighbor in the target
    tree_->nearestKSearchT(point, 1, nn_indices, nn_dists);
 
    // Deal with occlusions (incomplete targets)
    if (nn_dists[0] <= max_range) {
      // Add to the fitness score
      fitness_score += nn_dists[0];
      nr++;
    }
  }
 
  if (nr > 0)
    return (fitness_score / nr);
  return (std::numeric_limits<double>::max());
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
inline void
Registration<PointSource, PointTarget, Scalar>::align(PointCloudSource& output)
{
  align(output, Matrix4::Identity());
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
inline void
Registration<PointSource, PointTarget, Scalar>::align(PointCloudSource& output,
                                                      const Matrix4& guess)
{
  if (!initCompute())
    return;
 
  // Resize the output dataset
  output.resize(indices_->size());
  // Copy the header
  output.header = input_->header;
  // Check if the output will be computed for all points or only a subset
  if (indices_->size() != input_->size()) {
    output.width = indices_->size();
    output.height = 1;
  }
  else {
    output.width = static_cast<std::uint32_t>(input_->width);
    output.height = input_->height;
  }
  output.is_dense = input_->is_dense;
 
  // Copy the point data to output
  for (std::size_t i = 0; i < indices_->size(); ++i)
    output[i] = (*input_)[(*indices_)[i]];
 
  // Perform the actual transformation computation
  converged_ = false;
  final_transformation_ = transformation_ = previous_transformation_ =
      Matrix4::Identity();
 
  // Right before we estimate the transformation, we set all the point.data[3] values to
  // 1 to aid the rigid transformation
  for (std::size_t i = 0; i < indices_->size(); ++i)
    output[i].data[3] = 1.0;
 
  computeTransformation(output, guess);
 
  deinitCompute();
}
 
} // namespace pcl