pcl从一个点云里面导出下标

我们这次将学着使用ExtractIndices滤波器来从一个分割算法中导出点的下标。为了不把这个项目复杂化，我们不会在这里解释分割算法。

我们先建一个extract_indices.cpp

代码

#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>
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>);
// Fill in 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;
// Write the downsampled version to disk
pcl::PCDWriter writer;
writer.write<pcl::PointXYZ> ("table_scene_lms400_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 (0.01);
// 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 () > 0.3 * 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::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 << "table_scene_lms400_plane_" << i << ".pcd";
writer.write<pcl::PointXYZ> (ss.str (), *cloud_p, false);
// Create the filtering object
extract.setNegative (true);
extract.filter (*cloud_f);
cloud_filtered.swap (cloud_f);
i++;
}
return (0);

代码解释

首先我们用体元栅格滤波器来对数据进行降低采样。在这里，更少的点意味着花费更少的时间进行计算。

  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
sor.setInputCloud (cloud_blob);
sor.setLeafSize (0.01f, 0.01f, 0.01f);
sor.filter (*cloud_filtered_blob);

下一个代码块将处理参数分割。

  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 (0.01);

下面这行

 pcl::ExtractIndices<pcl::PointXYZ> extract;

和

    extract.setInputCloud (cloud_filtered);
extract.setIndices (inliers);
extract.setNegative (false);
extract.filter (*cloud_p);

代表了导出的滤波器后的真实的下标。为了处理多个模型，我们把这个教程在一个循环中进行处理，对于每一个被导出的模型，我们返回去获取指定的点，并且进行迭代，inliers(正常的好的点云)这个将在分割处理后获取。

运行结果

PointCloud before filtering: 460400 data points.
PointCloud after filtering: 41049 data points.
PointCloud representing the planar component: 20164 data points.
PointCloud representing the planar component: 12129 data points.