从一个点云中提取索引

附课本代码：

#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>intmain (int argc, char** argv){  sensor_msgs::PointCloud2::Ptr cloud_blob (new sensor_msgs::PointCloud2), cloud_filtered_blob (new sensor_msgs::PointCloud2);  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>);  // 填入点云数据  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;  // 创建滤波器对象:使用叶大小为1cm的下采样  pcl::VoxelGrid<sensor_msgs::PointCloud2> sor;  sor.setInputCloud (cloud_blob);  sor.setLeafSize (0.01f, 0.01f, 0.01f);  sor.filter (*cloud_filtered_blob);  // 转化为模板点云  pcl::fromROSMsg (*cloud_filtered_blob, *cloud_filtered);  std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height << " data points." << std::endl;  // 将下采样后的数据存入磁盘  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 ());  // 创建分割对象  pcl::SACSegmentation<pcl::PointXYZ> seg;  // 可选  seg.setOptimizeCoefficients (true);  // 必选  seg.setModelType (pcl::SACMODEL_PLANE);  seg.setMethodType (pcl::SAC_RANSAC);  seg.setMaxIterations (1000);  seg.setDistanceThreshold (0.01);  // 创建滤波器对象  pcl::ExtractIndices<pcl::PointXYZ> extract;  int i = 0, nr_points = (int) cloud_filtered->points.size ();  // 当还有30%原始点云数据时  while (cloud_filtered->points.size () > 0.3 * nr_points)  {    // 从余下的点云中分割最大平面组成部分    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;    }    // 分离内层    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);    // 创建滤波器对象    extract.setNegative (true);    extract.filter (*cloud_f);    cloud_filtered.swap (cloud_f);    i++;  }  return (0);}

相关对象函数说明：

1、pcl::PointIndices

This is the complete list of members for pcl::PointIndices, including all inherited members.ConstPtr typedefpcl::PointIndices headerpcl::PointIndices indicespcl::PointIndices PointIndices()pcl::PointIndices Ptr typedefpcl::PointIndices
2、SACSegmentation对象

（1）void pcl::SACSegmentation<PointT >::setOptimizeCoefficients(bool optimize)

Set to true if a coefficient refinement is required.//设置对估计的模型参数进行优化处理

Parameters:

[in]optimizetrue for enabling model coefficient refinement, false otherwise  

（2）void pcl::SACSegmentation<PointT >::setModelType(int model)

The type of model to use (user given parameter).

Parameters:

[in]modelthe model type

（3）void pcl::SACSegmentation<PointT >::setMethodType(int method)

The type of sample consensus method to use (user given parameter).

Parameters:

[in]methodthe method type 

    * SAC_RANSAC - RANdom SAmple Consensus
    * SAC_LMEDS - Least Median of Squares
    * SAC_MSAC - M-Estimator SAmple Consensus
    * SAC_RRANSAC - Randomized RANSAC
    * SAC_RMSAC - Randomized MSAC
    * SAC_MLESAC - Maximum LikeLihood Estimation SAmple Consensus
    * SAC_PROSAC - PROgressive SAmple Consensus

（4）void pcl::SACSegmentation< PointT >::setMaxIterations(int max_iterations)
Set the maximum number of iterations before giving up. 
Parameters:
[in]max_iterationsthe maximum number of iterations the sample consensus method will run   （5）
void pcl::SACSegmentation< PointT >::setDistanceThreshold(double threshold)
Distance to the model threshold (user given parameter). //设置判断是否为模型内点的距离阈值
Parameters:
[in]thresholdthe distance threshold to use 
（6）
virtual void pcl::PCLBase< PointT >::setInputCloud(const PointCloudConstPtr & cloud)
Provide a pointer to the input dataset. 
Parameters:
cloudthe const boost shared pointer to a PointCloud message  
（7）void pcl::SACSegmentation< PointT >::segment(PointIndices & inliers,  ModelCoefficients & model_coefficients  )
Base method for segmentation of a model in a PointCloud given by <setInputCloud (), setIndices ()> 
Parameters:
[in]inliersthe resultant point indices that support the model found (inliers)[out]model_coefficientsthe resultant model coefficients 
3、ExtractIndices滤波器对象
extracts a set of indices from a point cloud.
（1）void pcl::PCLBase< sensor_msgs::PointCloud2 >::setIndices(const PointIndicesConstPtr & indices)
Provide a pointer to the vector of indices that represents the input data. 
Parameters:
indicesa pointer to the vector of indices that represents the input data.  
（2）
void pcl::PCLBase< sensor_msgs::PointCloud2 >::setInputCloud(const PointCloud2ConstPtr & cloud) 
Provide a pointer to the input dataset. 
Parameters:
cloudthe const boost shared pointer to a PointCloud message  
（3）
void pcl::FilterIndices< sensor_msgs::PointCloud2 >::setNegative(bool negative)
Set whether the regular conditions for points filtering should apply, or the inverted conditions. 
Parameters:
[in]negativefalse = normal filter behavior (default), true = inverted behavior.   （4）
virtual void pcl::FilterIndices< sensor_msgs::PointCloud2 >::filter(PointCloud2 & output)
Calls the filtering method and returns the filtered dataset in output. 
void pcl::FilterIndices< sensor_msgs::PointCloud2 >::filter(std::vector< int > & indices) 
Calls the filtering method and returns the filtered point cloud indices.