iterative_closest_point.cpp

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 * $Id: normal_estimation.cpp 6746 2012-08-08 01:20:30Z rusu $
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#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/console/print.h>
#include <pcl/console/time.h>
#include <pcl/console/parse.h>
#include <pcl/registration/icp.h>
#include <pcl/registration/correspondence_estimation.h>
#include <pcl/registration/correspondence_estimation_normal_shooting.h>
#include <pcl/registration/transformation_estimation_lm.h>

#include <pcl/registration/correspondence_rejection_one_to_one.h>
#include <pcl/registration/correspondence_rejection_median_distance.h>
#include <pcl/registration/correspondence_rejection_sample_consensus.h>
#include <pcl/registration/correspondence_rejection_trimmed.h>
#include <pcl/registration/correspondence_rejection_var_trimmed.h>

using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;
using namespace pcl::registration;

Eigen::Vector4f    translation;
Eigen::Quaternionf orientation;

void
printHelp (int, char **argv)
{
  print_error ("Syntax is: %s input_source.pcd input_target.pcd output.pcd [optional_arguments]\n", argv[0]);
}

bool
loadCloud (const std::string &filename, pcl::PCLPointCloud2 &cloud)
{
  TicToc tt;
  print_highlight ("Loading "); print_value ("%s ", filename.c_str ());

  tt.tic ();
  if (loadPCDFile (filename, cloud, translation, orientation) < 0)
    return (false);
  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", cloud.width * cloud.height); print_info (" points]\n");
  print_info ("Available dimensions: "); print_value ("%s\n", getFieldsList (cloud).c_str ());

  return (true);
}

void
compute (const pcl::PCLPointCloud2::ConstPtr &source,
         const pcl::PCLPointCloud2::ConstPtr &target,
         pcl::PCLPointCloud2 &transformed_source)
{
  // Convert data to PointCloud<T>
  PointCloud<PointNormal>::Ptr src (new PointCloud<PointNormal>);
  PointCloud<PointNormal>::Ptr tgt (new PointCloud<PointNormal>);
  fromPCLPointCloud2 (*source, *src);
  fromPCLPointCloud2 (*target, *tgt);

  // Estimate
  TicToc tt;
  tt.tic ();
  
  print_highlight (stderr, "Computing ");

#define Scalar double
//#define Scalar float

  TransformationEstimationLM<PointNormal, PointNormal, Scalar>::Ptr te (new TransformationEstimationLM<PointNormal, PointNormal, Scalar>);
  //TransformationEstimationSVD<PointNormal, PointNormal, Scalar>::Ptr te (new TransformationEstimationSVD<PointNormal, PointNormal, Scalar>);
  CorrespondenceEstimation<PointNormal, PointNormal, double>::Ptr cens (new CorrespondenceEstimation<PointNormal, PointNormal, double>);
  //CorrespondenceEstimationNormalShooting<PointNormal, PointNormal, PointNormal>::Ptr cens (new CorrespondenceEstimationNormalShooting<PointNormal, PointNormal, PointNormal>);
  //CorrespondenceEstimationNormalShooting<PointNormal, PointNormal, PointNormal, double>::Ptr cens (new CorrespondenceEstimationNormalShooting<PointNormal, PointNormal, PointNormal, double>);
  cens->setInputSource (src);
  cens->setInputTarget (tgt);
  //cens->setSourceNormals (src);

  CorrespondenceRejectorOneToOne::Ptr cor_rej_o2o (new CorrespondenceRejectorOneToOne);
  
  CorrespondenceRejectorMedianDistance::Ptr cor_rej_med (new CorrespondenceRejectorMedianDistance);
  cor_rej_med->setInputSource<PointNormal> (src);
  cor_rej_med->setInputTarget<PointNormal> (tgt);

  CorrespondenceRejectorSampleConsensus<PointNormal>::Ptr cor_rej_sac (new CorrespondenceRejectorSampleConsensus<PointNormal>);
  cor_rej_sac->setInputSource (src);
  cor_rej_sac->setInputTarget (tgt);
  cor_rej_sac->setInlierThreshold (0.005);
  cor_rej_sac->setMaximumIterations (10000);

  CorrespondenceRejectorVarTrimmed::Ptr cor_rej_var (new CorrespondenceRejectorVarTrimmed);
  cor_rej_var->setInputSource<PointNormal> (src);
  cor_rej_var->setInputTarget<PointNormal> (tgt);
  
  CorrespondenceRejectorTrimmed::Ptr cor_rej_tri (new CorrespondenceRejectorTrimmed);

  IterativeClosestPoint<PointNormal, PointNormal, Scalar> icp;
  icp.setCorrespondenceEstimation (cens);
  icp.setTransformationEstimation (te);
  icp.addCorrespondenceRejector (cor_rej_o2o);
  //icp.addCorrespondenceRejector (cor_rej_var);
  //icp.addCorrespondenceRejector (cor_rej_med);
  //icp.addCorrespondenceRejector (cor_rej_tri);
  //icp.addCorrespondenceRejector (cor_rej_sac);
  icp.setInputSource (src);
  icp.setInputTarget (tgt);
  icp.setMaximumIterations (1000);
  icp.setTransformationEpsilon (1e-10);
  PointCloud<PointNormal> output;
  icp.align (output);

  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", output.width * output.height); print_info (" points], has converged: ");
  print_value ("%d", icp.hasConverged ()); print_info (" with score: %f\n",  icp.getFitnessScore ());
  Eigen::Matrix4d transformation = icp.getFinalTransformation ();
  //Eigen::Matrix4f transformation = icp.getFinalTransformation ();
  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", 
      transformation (0, 0), transformation (0, 1), transformation (0, 2), transformation (0, 3),
      transformation (1, 0), transformation (1, 1), transformation (1, 2), transformation (1, 3),
      transformation (2, 0), transformation (2, 1), transformation (2, 2), transformation (2, 3),
      transformation (3, 0), transformation (3, 1), transformation (3, 2), transformation (3, 3));

  // Convert data back
  pcl::PCLPointCloud2 output_source;
  toPCLPointCloud2 (output, output_source);
  concatenateFields (*source, output_source, transformed_source);
}

void
saveCloud (const std::string &filename, const pcl::PCLPointCloud2 &output)
{
  TicToc tt;
  tt.tic ();

  print_highlight ("Saving "); print_value ("%s ", filename.c_str ());

  PCDWriter w;
  //w.writeBinaryCompressed (filename, output, translation, orientation);
  w.writeASCII (filename, output, translation, orientation);
  
  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", output.width * output.height); print_info (" points]\n");
}

int
main (int argc, char** argv)
{
  print_info ("Estimate a rigid transformation using IterativeClosestPoint. For more information, use: %s -h\n", argv[0]);

  if (argc < 3)
  {
    printHelp (argc, argv);
    return (-1);
  }
  
  bool debug = false;
  pcl::console::parse_argument (argc, argv, "-debug", debug);
  if (debug)
    pcl::console::setVerbosityLevel (pcl::console::L_DEBUG);

  // Parse the command line arguments for .pcd files
  std::vector<int> p_file_indices;
  p_file_indices = parse_file_extension_argument (argc, argv, ".pcd");
  if (p_file_indices.size () != 3)
  {
    print_error ("Need two input PCD files (source, target) and one output PCD file to continue.\n");
    return (-1);
  }

  // Load the input files
  pcl::PCLPointCloud2::Ptr src (new pcl::PCLPointCloud2);
  if (!loadCloud (argv[p_file_indices[0]], *src)) return (-1);
  pcl::PCLPointCloud2::Ptr tgt (new pcl::PCLPointCloud2);
  if (!loadCloud (argv[p_file_indices[1]], *tgt)) return (-1);

  // Perform the feature estimation
  pcl::PCLPointCloud2 output;
  compute (src, tgt, output);

  // Save into the output file
  saveCloud (argv[p_file_indices[2]], output);

  return (0);
}