Plane model segmentation + Normals estimation for each plane

#include <iostream>
#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/features/normal_3d.h>
#include <pcl/visualization/cloud_viewer.h>

#include <pcl/io/io.h>
#include <pcl/features/integral_image_normal.h>

int main (int argc, char** argv)
{
  pcl::PCLPointCloud2::Ptr cloud_blob (new pcl::PCLPointCloud2), cloud_filtered_blob (new pcl::PCLPointCloud2);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>), cloud_p (new
  pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>);

  // Load the cloud data
  pcl::PCDReader reader;
  reader.read ("table_scene_lms400.pcd", *cloud_blob);
  std::cerr << "PointCloud before filtering: " << cloud_blob->width * cloud_blob->height << " data points." << std::endl;

  // Create the filtering object: downsample the dataset using a leaf size of 1cm
  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
  sor.setInputCloud (cloud_blob);
  sor.setLeafSize (0.01f, 0.01f, 0.01f);
  sor.filter (*cloud_filtered_blob);

  // Convert to the templated PointCloud
  pcl::fromPCLPointCloud2 (*cloud_filtered_blob, *cloud_filtered);
  std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height << " data points." << std::endl;

  pcl::PCDWriter writer;

  // Write the downsampled version to disk
  //writer.write<pcl::PointXYZ> ("downsampled.pcd", *cloud_filtered, false);

  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
  pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());

  // Create the segmentation object
  pcl::SACSegmentation<pcl::PointXYZ> seg;

  // Optional
  seg.setOptimizeCoefficients (true);

  // Mandatory
  seg.setModelType (pcl::SACMODEL_PLANE);
  seg.setMethodType (pcl::SAC_RANSAC);
  seg.setMaxIterations (1000);
  seg.setDistanceThreshold (0.05);

  // Create the filtering object
  pcl::ExtractIndices<pcl::PointXYZ> extract;

  int i = 0, nr_points = (int) cloud_filtered->points.size ();

  // While 30% of the original cloud is still there
  while (cloud_filtered->points.size () > 0.1 * nr_points)
  {
    // Segment the largest planar component from the remaining cloud
    seg.setInputCloud (cloud_filtered);
    seg.segment (*inliers, *coefficients);
    if (inliers->indices.size () == 0)
    {
      std::cerr << "Could not estimate a planar model for the given dataset." << std::endl;
      break;
    }

    // Extract the inliers
    extract.setInputCloud (cloud_filtered);
    extract.setIndices (inliers);
    extract.setNegative (false);
    extract.filter (*cloud_p);
    std::cerr << "PointCloud representing the planar component: " << cloud_p->width * cloud_p->height << " data points." << std::endl;

    std::stringstream ss;
    ss << "plane_" << i << ".pcd";
    writer.write<pcl::PointXYZ> (ss.str (), *cloud_p, false);

  // Create the normal estimation class, and pass the input dataset to it
  pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
  ne.setInputCloud (cloud_p);

  // Create an empty kdtree representation, and pass it to the normal estimation object.
  // Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
  pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
  ne.setSearchMethod (tree);

  // Output datasets
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);

  // Use all neighbors in a sphere of radius 3cm
  ne.setRadiusSearch (0.03);

  // Compute the features
  ne.compute (*cloud_normals);

//  std::cout << "Normals: " << cloud_normals->points[0] << std::endl;


  // visualize normals
  pcl::visualization::PCLVisualizer viewer("PCL Viewer");
  viewer.setBackgroundColor (0.0, 0.0, 0.5);
  viewer.addPointCloudNormals<pcl::PointXYZ,pcl::Normal>(cloud_p, cloud_normals);

  while (!viewer.wasStopped ())
  {
     viewer.spinOnce ();
  }

    // Create the filtering object
    extract.setNegative (true);
    extract.filter (*cloud_f);
    cloud_filtered.swap (cloud_f);
    i++;
  }

  return (0);
}