点云滤波---半径滤波器

适用对象

滤除离群点的一种滤波方法。

工作原理

通过设定滤波半径，计算每个点在其半径范围内的其他点的个数。半径范围内其他点个数少于某一设定的阈值的点将被滤除。

如上图所示，假设设置半径为d，分别考察黄色、蓝色和绿色的三个点。若设置点个数阈值为1，则黄色点将被滤除；若设置点个数阈值为2，则黄色点和绿色点都将被滤除。

PCL核心代码实现

pcl::RadiusOutlierRemoval<pcl::PointXYZ> outrem;//创建半径滤波器对象
outrem.setInputCloud(pointCloud_raw);			//设置输入点云
outrem.setRadiusSearch(0.02);					//设置半径为2cm
outrem.setMinNeighborsInRadius(5);				//设置最小邻接点个数阈值,半径范围内其他点个数少于5的点将被滤除
outrem.filter(*pointCloud_filter);				//执行滤波

完整代码

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/filters/conditional_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);
  }

  if (strcmp(argv[1], "-r") == 0){
    pcl::RadiusOutlierRemoval<pcl::PointXYZ> outrem;
    // build the filter
    outrem.setInputCloud(cloud);
    outrem.setRadiusSearch(0.8);
    outrem.setMinNeighborsInRadius (2);
    // apply filter
    outrem.filter (*cloud_filtered);
  }
  else if (strcmp(argv[1], "-c") == 0){
    // build the condition
    pcl::ConditionAnd<pcl::PointXYZ>::Ptr range_cond (new
      pcl::ConditionAnd<pcl::PointXYZ> ());
    range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
      pcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::GT, 0.0)));
    range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
      pcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::LT, 0.8)));
    // build the filter
    pcl::ConditionalRemoval<pcl::PointXYZ> condrem;
    condrem.setCondition (range_cond);
    condrem.setInputCloud (cloud);
    condrem.setKeepOrganized(true);
    // apply filter
    condrem.filter (*cloud_filtered);
  }
  else{
    std::cerr << "please specify command line arg '-r' or '-c'" << std::endl;
    exit(0);
  }
  std::cerr << "Cloud before filtering: " << std::endl;
  for (size_t i = 0; i < cloud->points.size (); ++i)
    std::cerr << "    " << cloud->points[i].x << " "
                        << cloud->points[i].y << " "
                        << cloud->points[i].z << std::endl;
  // display pointcloud after filtering
  std::cerr << "Cloud after filtering: " << std::endl;
  for (size_t i = 0; i < cloud_filtered->points.size (); ++i)
    std::cerr << "    " << cloud_filtered->points[i].x << " "
                        << cloud_filtered->points[i].y << " "
                        << cloud_filtered->points[i].z << std::endl;
  return (0);
}

参考资料

Removing outliers using a Conditional or RadiusOutlier removal