从一个点云中提取索引

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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>
int
main (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 typedef	pcl::PointIndices
header	pcl::PointIndices
indices	pcl::PointIndices
PointIndices()	pcl::PointIndices
Ptr typedef	pcl::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] optimize true 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] model the model type

（3）

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

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

Parameters:

[in] method the 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_iterations the 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] threshold the distance threshold to use 
（6）
virtual void pcl::PCLBase< PointT
>::setInputCloud ( const PointCloudConstPtr &  cloud )
Provide a pointer to the input dataset. 
Parameters:cloud the 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] inliers the resultant point indices that support the model found (inliers)
[out] model_coefficients the 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:indices a 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:
cloud the 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] negative false = 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.