PCL点云分割（2）

关于点云的分割算是我想做的机械臂抓取中十分重要的俄一部分，所以首先学习如果使用点云库处理我用kinect获取的点云的数据，本例程也是我自己慢慢修改程序并结合官方API 的解说实现的，其中有很多细节如果直接更改源程序，可能会因为数据类型，或者头文件等各种原因编译不过，会导致我们比较难得找出其中的错误，首先我们看一下我自己设定的一个场景，然后我用kinect获取数据

观察到kinect获取的原始图像的，然后使用简单的滤波，把在其中的NANS点移除，因为很多的算法要求不能出现NANS点，我们可以看见这里面有充电宝，墨水，乒乓球，一双筷子，下面是两张纸，上面分别贴了两道黑色的胶带，我们首先就可以做一个提取原始点云的平面的实验，那么如果提取点云中平面，之前有一些基本的实例，使用平面分割法

程序如下

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

int
 main (int argc, char** argv)
{
// 读取文件 
  pcl::PCDReader reader;
  pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGBA>), cloud_f (new pcl::PointCloud<pcl::PointXYZRGBA>);
 pcl::PointCloud<pcl::PointXYZRGBA>::Ptr final (new pcl::PointCloud<pcl::PointXYZRGBA>);
  reader.read ("out0.pcd", *cloud);
  std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //*

// 下采样，体素叶子大小为0.01
  pcl::VoxelGrid<pcl::PointXYZRGBA> vg;
  pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZRGBA>);
  vg.setInputCloud (cloud);
  vg.setLeafSize (0.01f, 0.01f, 0.01f);
  vg.filter (*cloud_filtered);
  std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size ()  << " data points." << std::endl; //*
  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
  pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
// Create the segmentation object
  pcl::SACSegmentation<pcl::PointXYZRGBA> seg;
// Optional
  seg.setOptimizeCoefficients (true);
// Mandatory
  seg.setModelType (pcl::SACMODEL_PLANE);
//  seg.setModelType (pcl::SACMODEL_LINE );
  seg.setMethodType (pcl::SAC_RANSAC);
  seg.setDistanceThreshold (0.01);

  seg.setInputCloud (cloud_filtered);
  seg.segment (*inliers, *coefficients);

if (inliers->indices.size () == 0)
  {
    PCL_ERROR ("Could not estimate a planar model for the given dataset.");
return (-1);
  }

  std::cerr << "Model coefficients: " << coefficients->values[0] << " " 
                                      << coefficients->values[1] << " "
                                      << coefficients->values[2] << " " 
                                     << coefficients->values[3] <<std::endl;
return (0);
}

运行生成的可执行文件会输出平面模型的参数

                                                               平面模型的参数

                                                                      此图是采样后的点云图

也可以在这个程序中直接实现平面的提取，但是为了更好的说明，我是将获取平面参数与平面提取给分成两个程序实现，程序如下

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/ModelCoefficients.h>
#include <pcl/filters/project_inliers.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.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)
{

  boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
  viewer->setBackgroundColor (0, 0, 0);
  viewer->addPointCloud<pcl::PointXYZ> (cloud, "project_inliners 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)
{
// 读取文件 
  pcl::PCDReader reader;
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected (new pcl::PointCloud<pcl::PointXYZ>);

 pcl::PointCloud<pcl::PointXYZ>::Ptr final (new pcl::PointCloud<pcl::PointXYZ>);
  reader.read ("out0.pcd", *cloud);
  std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //*

// 下采样，体素叶子大小为0.01
  pcl::VoxelGrid<pcl::PointXYZ> vg;
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
  vg.setInputCloud (cloud);
  vg.setLeafSize (0.01f, 0.01f, 0.01f);
  vg.filter (*cloud_filtered);
  std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size ()  << " data points." << std::endl; //*

// Create a set of planar coefficients with X=Y=
  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
  coefficients->values.resize (4);
  coefficients->values[0] = 0.140101;
  coefficients->values[1] = 0.126715;
  coefficients->values[2] = 0.981995;
  coefficients->values[3] = -0.702224;

// Create the filtering object
  pcl::ProjectInliers<pcl::PointXYZ> proj;
  proj.setModelType (pcl::SACMODEL_PLANE);
  proj.setInputCloud (cloud_filtered);
  proj.setModelCoefficients (coefficients);
  proj.filter (*cloud_projected);

 boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer;
  viewer = simpleVis(cloud_projected);
while (!viewer->wasStopped ())
  {
    viewer->spinOnce (100);
    boost::this_thread::sleep (boost::posix_time::microseconds (100000));
  }

return (0);
}

执行结果就如下

提取了平面，**********************8

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