Sample Consensus (SAC)

How to use Random Sample Consensus model (RANSAC)

#include <iostream>
#include <pcl/console/parse.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/sample_consensus/sac_model_sphere.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <boost/thread/thread.hpp>

boost::shared_ptr<pcl::visualization::PCLVisualizer>
simpleVis (pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{
  //Open 3D viewer and add point cloud
  boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
  viewer->setBackgroundColor (0, 0, 0);
  viewer->addPointCloud<pcl::PointXYZ> (cloud, "sample cloud");
  viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");
  //viewer->addCoordinateSystem (1.0, "global");
  viewer->initCameraParameters ();
  return (viewer);
}

int main(int argc, char** argv)
{
  // initialize PointClouds
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr final (new pcl::PointCloud<pcl::PointXYZ>);

  // populate our PointCloud with points
  cloud->width    = 500;
  cloud->height   = 1;
  cloud->is_dense = false;
  cloud->points.resize (cloud->width * cloud->height);
  for (size_t i = 0; i < cloud->points.size (); ++i)
  {
    if (pcl::console::find_argument (argc, argv, "-s") >= 0 || pcl::console::find_argument (argc, argv, "-sf") >= 0)
    {
      cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0);
      cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0);
      if (i % 5 == 0)
        cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0);
      else if(i % 2 == 0)
        cloud->points[i].z =  sqrt( 1 - (cloud->points[i].x * cloud->points[i].x)
                                      - (cloud->points[i].y * cloud->points[i].y));
      else
        cloud->points[i].z =  - sqrt( 1 - (cloud->points[i].x * cloud->points[i].x)
                                        - (cloud->points[i].y * cloud->points[i].y));
    }
    else
    {
      cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0);
      cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0);
      if( i % 2 == 0)
        cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0);
      else
        cloud->points[i].z = -1 * (cloud->points[i].x + cloud->points[i].y);
    }
  }

  std::vector<int> inliers;

  //創建RandomSampleConsensus物件
  pcl::SampleConsensusModelSphere<pcl::PointXYZ>::Ptr
    model_s(new pcl::SampleConsensusModelSphere<pcl::PointXYZ> (cloud));
  pcl::SampleConsensusModelPlane<pcl::PointXYZ>::Ptr
    model_p (new pcl::SampleConsensusModelPlane<pcl::PointXYZ> (cloud));

  //選擇擬和平面或是球
  if(pcl::console::find_argument (argc, argv, "-f") >= 0)
  {
    pcl::RandomSampleConsensus<pcl::PointXYZ> ransac (model_p); //指定採用平面模型model_p來隨機擬和
    ransac.setDistanceThreshold (.01); //距離超出1cm者不屬於平面模型
    ransac.computeModel(); //擬合平面模型
    ransac.getInliers(inliers); //取得屬於平面模型內的點群inliners
  }
  else if (pcl::console::find_argument (argc, argv, "-sf") >= 0 )
  {
    pcl::RandomSampleConsensus<pcl::PointXYZ> ransac (model_s); //指定採用球模型model_s來隨機擬和
    ransac.setDistanceThreshold (.01); //距離超出1cm者不屬於平面模型
    ransac.computeModel(); //擬合球模型
    ransac.getInliers(inliers);  //取得屬於球模型內的點群inliners
  }

  //將cloud點雲中的inliners索引點複製到final點雲
  pcl::copyPointCloud<pcl::PointXYZ>(*cloud, inliers, *final);

  //創建視窗物件，將原始點雲或inliers展示出
  boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer;
  if (pcl::console::find_argument (argc, argv, "-f") >= 0 || pcl::console::find_argument (argc, argv, "-sf") >= 0)
    viewer = simpleVis(final);
  else
    viewer = simpleVis(cloud);
  while (!viewer->wasStopped ())
  {
    viewer->spinOnce (100);
    boost::this_thread::sleep (boost::posix_time::microseconds (100000));
  }
  return 0;
 }

Planar model

typedef pcl::PointXYZ PointT;

pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>);

pcl::SACSegmentation<PointT> seg;
pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
pcl::PointCloud<PointT>::Ptr cloud_plane (new pcl::PointCloud<PointT> ());

seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (100);
seg.setDistanceThreshold (0.02);

int i=0, nr_points = (int) cloud_filtered->points.size ();
  while (cloud_filtered->points.size () > 0.3 * 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::cout << "Could not estimate a planar model for the given dataset." << std::endl;
      break;
    }

    // Extract the planar inliers from the input cloud
    pcl::ExtractIndices<PointT> extract;
    extract.setInputCloud (cloud_filtered);
    extract.setIndices (inliers);
    extract.setNegative (false);

    // Get the points associated with the planar surface
    extract.filter (*cloud_plane);
    std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl;

    // Remove the planar inliers, extract the rest
    extract.setNegative (true);
    extract.filter (*cloud_f);
    *cloud_filtered = *cloud_f;
  }