PassThrough filter

In this tutorial we will learn how to perform a simple filtering along a specified dimension – that is, cut off values that are either inside or outside a given user range.

#include <iostream>

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

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

  // Fill in the cloud data
  cloud->width  = 5;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
  }

  // Create the filtering object
  pcl::PassThrough<pcl::PointXYZ> pass;
  pass.setInputCloud (cloud);
  pass.setFilterFieldName ("z");
  pass.setFilterLimits (0.0, 1.0);
  //pass.setFilterLimitsNegative (true);
  pass.filter (*cloud_filtered);

  return (0);
}

注：pass.setFilterLimitsNegative (true);是对范围取反

VoxelGrid filter

The VoxelGrid class that we’re about to present creates a 3D voxel grid (think about a voxel grid as a set of tiny 3D boxes in space) over the input point cloud data. Then, in each voxel (i.e., 3D box), all the points present will be approximated (i.e., downsampled) with their centroid. This approach is a bit slower than approximating them with the center of the voxel, but it represents the underlying surface more accurately.

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>

int main (int argc, char** argv)
{
  pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2 ());
  pcl::PCLPointCloud2::Ptr cloud_filtered (new pcl::PCLPointCloud2 ());

  // Fill in the cloud data
  pcl::PCDReader reader;
  // Replace the path below with the path where you saved your file
  reader.read ("table_scene_lms400.pcd", *cloud); // Remember to download the file first!

  // Create the filtering object
  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
  sor.setInputCloud (cloud);
  sor.setLeafSize (0.01f, 0.01f, 0.01f);
  sor.filter (*cloud_filtered);

  pcl::PCDWriter writer;
  writer.write ("table_scene_lms400_downsampled.pcd", *cloud_filtered, 
         Eigen::Vector4f::Zero (), Eigen::Quaternionf::Identity (), false);

  return (0);
}

StatisticalOutlierRemoval filter

StatisticalOutlierRemoval filter is based on the computation of the distribution of point to neighbors distances in the input dataset. For each point, we compute the mean distance from it to all its neighbors. By assuming that the resulted distribution is Gaussian with a mean and a standard deviation, all points whose mean distances are outside an interval defined by the global distances mean and standard deviation can be considered as outliers and trimmed from the dataset.

#include <iostream>

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

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

  // Fill in the cloud data
  pcl::PCDReader reader;
  // Replace the path below with the path where you saved your file
  reader.read<pcl::PointXYZ> ("table_scene_lms400.pcd", *cloud);

  std::cerr << "Cloud before filtering: " << std::endl;
  std::cerr << *cloud << std::endl;

  // Create the filtering object
  pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor;
  sor.setInputCloud (cloud);
  sor.setMeanK (50);
  sor.setStddevMulThresh (1.0);
  sor.filter (*cloud_filtered);

  std::cerr << "Cloud after filtering: " << std::endl;
  std::cerr << *cloud_filtered << std::endl;

  pcl::PCDWriter writer;
  writer.write<pcl::PointXYZ> ("table_scene_lms400_inliers.pcd", *cloud_filtered, false);

  //sor.setNegative (true);
  //sor.filter (*cloud_filtered);
  //writer.write<pcl::PointXYZ> ("table_scene_lms400_outliers.pcd", *cloud_filtered, false);

  return (0);
}

注：sor.setMeanK (50); 参数为临近点的个数
注：sor.setStddevMulThresh (1.0);为平均差系数distance_thres = means + std * StddevMulThresh;
注：sor.setNegative (true);输出被滤掉的点云

RadiusOutlier removal

The user specifies a number of neighbors which every index must have within a specified radius to remain in the PointCloud.

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/filters/radius_outlier_removal.h>

int main (int argc, char** argv)
{
  if (argc != 2)
  {
    std::cerr << "please specify command line arg ‘-r‘ or ‘-c‘" << std::endl;
    exit(0);
  }
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // Fill in the cloud data
  cloud->width  = 5;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
  }
  
  pcl::RadiusOutlierRemoval<pcl::PointXYZ> outrem;
  // build the filter
  outrem.setInputCloud(cloud);
  outrem.setRadiusSearch(0.8);
  outrem.setMinNeighborsInRadius (2);
  // apply filter
  outrem.filter (*cloud_filtered);
  
  return (0);

pcl之filtering滤波