segmentation_cylinder.cpp

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 *     with the distribution.
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 *     contributors may be used to endorse or promote products derived
 *     from this software without specific prior written permission.
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 * $Id: segmentation_cylinder.cpp 35522 2011-01-26 08:17:01Z rusu $
 *
 */
#include <pcl/ModelCoefficients.h>

#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>

#include <pcl/features/normal_3d.h>

#include <pcl/filters/extract_indices.h>
#include <pcl/filters/passthrough.h>

#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>

typedef pcl::PointXYZ PointT;

/* ---[ */
int
  main (int argc, char** argv)
{
  // All the objects needed
  /*****************************************************
   * TODO 1: What do these classes do - document next lines
   * Documentation http://www.ros.org/doc/api/pcl/html/annotated.html
   */
  pcl::PCDReader reader;
  pcl::PassThrough<PointT> pass;
  pcl::NormalEstimation<PointT, pcl::Normal> ne;
  pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg;
  pcl::PCDWriter writer;
  pcl::ExtractIndices<PointT> extract;
  pcl::ExtractIndices<pcl::Normal> extract_normals;
  pcl::KdTreeFLANN<PointT>::Ptr tree (new pcl::KdTreeFLANN<PointT> ());

  // Datasets
  pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT> ());
  pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT> ());
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal> ());
  pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients ()), coefficients_cylinder (new pcl::ModelCoefficients ());
  pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices ()), inliers_cylinder (new pcl::PointIndices ());

  /*****************************************************
   * TODO 2: Read in the input pointcloud: data/table_scene_mug_stereo_textured.pcd
   * Use "reader" object
   */


  // Build a passthrough filter to remove spurious NaNs
  pass.setInputCloud (cloud);
  pass.setFilterFieldName ("z");
  pass.setFilterLimits (0, 1.5);
  pass.filter (*cloud_filtered);
  ROS_INFO ("PointCloud after filtering has: %zu data points.", cloud_filtered->points.size ());


  /*****************************************************
   * TODO 3: Estimate the normals
   * Use "ne" object, methods to be called in order: setSearchMethod, setInputCloud, setKSearch, compute
   */


    /*****************************************************
   * TODO 4: Create the segmentation object for the planar model and set all the parameters
   * Use "seg" object, methods to be called in order: setOptimizeCoefficients, setNormalDistanceWeight, 
   * setMethodType (use SAC_RANSAC), setMaxIterations, setDistanceThreshold, setInputCloud, segment 
   */


  /*****************************************************
   * TODO 5: print out plane coefficients
   */
  std::cerr << "Plane coefficients: " << /*todo*/ << std::endl;


  // Extract the planar inliers from the input cloud
  extract.setInputCloud (cloud_filtered);
  extract.setIndices (inliers_plane);
  extract.setNegative (false);


  pcl::PointCloud<PointT>::Ptr cloud_plane (new pcl::PointCloud<PointT> ());
  extract.filter (*cloud_plane);
  ROS_INFO ("PointCloud representing the planar component: %zu data points.", cloud_plane->points.size ());
  /*****************************************************
   * TODO 6:  Write the planar inliers to disk
   * Use "writer" object
   */


    /*****************************************************
   * TODO 7:  Remove the planar inliers, extract the cluster lying on a table
   * Use "extract" object and use the following functions in order setNegative, filter
   */


  /*****************************************************
   * TODO 8:  Write the cluster to disk
   */

  return (0);
}
/* ]--- */