icp.hpp

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#ifndef PCL_REGISTRATION_IMPL_ICP_HPP_
#define PCL_REGISTRATION_IMPL_ICP_HPP_

#include <pcl/registration/boost.h>
#include <pcl/correspondence.h>

template <typename PointSource, typename PointTarget, typename Scalar> void
pcl::IterativeClosestPoint<PointSource, PointTarget, Scalar>::transformCloud (
    const PointCloudSource &input, 
    PointCloudSource &output, 
    const Matrix4 &transform)
{
  Eigen::Vector4f pt (0.0f, 0.0f, 0.0f, 1.0f), pt_t;
  Eigen::Matrix4f tr = transform.template cast<float> ();

  // XYZ is ALWAYS present due to the templatization, so we only have to check for normals
  if (source_has_normals_)
  {
    Eigen::Vector3f nt, nt_t;
    Eigen::Matrix3f rot = tr.block<3, 3> (0, 0);

    for (size_t i = 0; i < input.size (); ++i)
    {
      const uint8_t* data_in = reinterpret_cast<const uint8_t*> (&input[i]);
      uint8_t* data_out = reinterpret_cast<uint8_t*> (&output[i]);
      memcpy (&pt[0], data_in + x_idx_offset_, sizeof (float));
      memcpy (&pt[1], data_in + y_idx_offset_, sizeof (float));
      memcpy (&pt[2], data_in + z_idx_offset_, sizeof (float));

      if (!pcl_isfinite (pt[0]) || !pcl_isfinite (pt[1]) || !pcl_isfinite (pt[2])) 
        continue;

      pt_t = tr * pt;

      memcpy (data_out + x_idx_offset_, &pt_t[0], sizeof (float));
      memcpy (data_out + y_idx_offset_, &pt_t[1], sizeof (float));
      memcpy (data_out + z_idx_offset_, &pt_t[2], sizeof (float));

      memcpy (&nt[0], data_in + nx_idx_offset_, sizeof (float));
      memcpy (&nt[1], data_in + ny_idx_offset_, sizeof (float));
      memcpy (&nt[2], data_in + nz_idx_offset_, sizeof (float));

      if (!pcl_isfinite (nt[0]) || !pcl_isfinite (nt[1]) || !pcl_isfinite (nt[2])) 
        continue;

      nt_t = rot * nt;

      memcpy (data_out + nx_idx_offset_, &nt_t[0], sizeof (float));
      memcpy (data_out + ny_idx_offset_, &nt_t[1], sizeof (float));
      memcpy (data_out + nz_idx_offset_, &nt_t[2], sizeof (float));
    }
  }
  else
  {
    for (size_t i = 0; i < input.size (); ++i)
    {
      const uint8_t* data_in = reinterpret_cast<const uint8_t*> (&input[i]);
      uint8_t* data_out = reinterpret_cast<uint8_t*> (&output[i]);
      memcpy (&pt[0], data_in + x_idx_offset_, sizeof (float));
      memcpy (&pt[1], data_in + y_idx_offset_, sizeof (float));
      memcpy (&pt[2], data_in + z_idx_offset_, sizeof (float));

      if (!pcl_isfinite (pt[0]) || !pcl_isfinite (pt[1]) || !pcl_isfinite (pt[2])) 
        continue;

      pt_t = tr * pt;

      memcpy (data_out + x_idx_offset_, &pt_t[0], sizeof (float));
      memcpy (data_out + y_idx_offset_, &pt_t[1], sizeof (float));
      memcpy (data_out + z_idx_offset_, &pt_t[2], sizeof (float));
    }
  }
  
}

template <typename PointSource, typename PointTarget, typename Scalar> void
pcl::IterativeClosestPoint<PointSource, PointTarget, Scalar>::computeTransformation (
    PointCloudSource &output, const Matrix4 &guess)
{
  // Point cloud containing the correspondences of each point in <input, indices>
  PointCloudSourcePtr input_transformed (new PointCloudSource);

  nr_iterations_ = 0;
  converged_ = false;

  // Initialise final transformation to the guessed one
  final_transformation_ = guess;

  // If the guessed transformation is non identity
  if (guess != Matrix4::Identity ())
  {
    input_transformed->resize (input_->size ());
     // Apply guessed transformation prior to search for neighbours
    transformCloud (*input_, *input_transformed, guess);
  }
  else
    *input_transformed = *input_;
 
  transformation_ = Matrix4::Identity ();

  // Pass in the default target for the Correspondence Estimation/Rejection code
  correspondence_estimation_->setInputTarget (target_);
  // We should be doing something like this
  // for (size_t i = 0; i < correspondence_rejectors_.size (); ++i)
  // {
  //   correspondence_rejectors_[i]->setTargetCloud (target_);
  //   if (target_has_normals_)
  //     correspondence_rejectors_[i]->setTargetNormals (target_);
  // }

  convergence_criteria_->setMaximumIterations (max_iterations_);
  convergence_criteria_->setRelativeMSE (euclidean_fitness_epsilon_);
  convergence_criteria_->setTranslationThreshold (transformation_epsilon_);
  convergence_criteria_->setRotationThreshold (1.0 - transformation_epsilon_);

  // Repeat until convergence
  do
  {
    // Save the previously estimated transformation
    previous_transformation_ = transformation_;

    // Set the source each iteration, to ensure the dirty flag is updated
    correspondence_estimation_->setInputSource (input_transformed);
    // Estimate correspondences
    if (use_reciprocal_correspondence_)
      correspondence_estimation_->determineReciprocalCorrespondences (*correspondences_, corr_dist_threshold_);
    else
      correspondence_estimation_->determineCorrespondences (*correspondences_, corr_dist_threshold_);

    //if (correspondence_rejectors_.empty ())
    CorrespondencesPtr temp_correspondences (new Correspondences (*correspondences_));
    for (size_t i = 0; i < correspondence_rejectors_.size (); ++i)
    {
      PCL_DEBUG ("Applying a correspondence rejector method: %s.\n", correspondence_rejectors_[i]->getClassName ().c_str ());
      // We should be doing something like this
      // correspondence_rejectors_[i]->setInputSource (input_transformed);
      // if (source_has_normals_)
      //   correspondence_rejectors_[i]->setInputNormals (input_transformed);
      correspondence_rejectors_[i]->setInputCorrespondences (temp_correspondences);
      correspondence_rejectors_[i]->getCorrespondences (*correspondences_);
      // Modify input for the next iteration
      if (i < correspondence_rejectors_.size () - 1)
        *temp_correspondences = *correspondences_;
    }

    size_t cnt = correspondences_->size ();
    // Check whether we have enough correspondences
    if (cnt < min_number_correspondences_)
    {
      PCL_ERROR ("[pcl::%s::computeTransformation] Not enough correspondences found. Relax your threshold parameters.\n", getClassName ().c_str ());
      convergence_criteria_->setConvergenceState(pcl::registration::DefaultConvergenceCriteria<Scalar>::CONVERGENCE_CRITERIA_NO_CORRESPONDENCES);
      converged_ = false;
      break;
    }

    // Estimate the transform
    transformation_estimation_->estimateRigidTransformation (*input_transformed, *target_, *correspondences_, transformation_);

    // Tranform the data
    transformCloud (*input_transformed, *input_transformed, transformation_);

    // Obtain the final transformation    
    final_transformation_ = transformation_ * final_transformation_;

    ++nr_iterations_;

    // Update the vizualization of icp convergence
    //if (update_visualizer_ != 0)
    //  update_visualizer_(output, source_indices_good, *target_, target_indices_good );

    converged_ = static_cast<bool> ((*convergence_criteria_));
  }
  while (!converged_);

  // Transform the input cloud using the final transformation
  PCL_DEBUG ("Transformation is:\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n", 
      final_transformation_ (0, 0), final_transformation_ (0, 1), final_transformation_ (0, 2), final_transformation_ (0, 3),
      final_transformation_ (1, 0), final_transformation_ (1, 1), final_transformation_ (1, 2), final_transformation_ (1, 3),
      final_transformation_ (2, 0), final_transformation_ (2, 1), final_transformation_ (2, 2), final_transformation_ (2, 3),
      final_transformation_ (3, 0), final_transformation_ (3, 1), final_transformation_ (3, 2), final_transformation_ (3, 3));

  // Copy all the values
  output = *input_;
  // Transform the XYZ + normals
  transformCloud (*input_, output, final_transformation_);
}

template <typename PointSource, typename PointTarget, typename Scalar> void
pcl::IterativeClosestPointWithNormals<PointSource, PointTarget, Scalar>::transformCloud (
    const PointCloudSource &input, 
    PointCloudSource &output, 
    const Matrix4 &transform)
{
  pcl::transformPointCloudWithNormals (input, output, transform);
}

#endif /* PCL_REGISTRATION_IMPL_ICP_HPP_ */