我們這次將學著使用ExtractIndices濾波器來從一個分割算法中導出點的下標。為了不把這個項目復雜化，我們不會在這里解釋分割算法。

我們先建一個extract_indices.cpp

代碼

#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 datapcl::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 1cmpcl::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 PointCloudpcl::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 diskpcl::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 objectpcl::SACSegmentation<pcl::PointXYZ> seg;// Optionalseg.setOptimizeCoefficients (true);// Mandatoryseg.setModelType (pcl::SACMODEL_PLANE);seg.setMethodType (pcl::SAC_RANSAC);seg.setMaxIterations (1000);seg.setDistanceThreshold (0.01);// Create the filtering objectpcl::ExtractIndices<pcl::PointXYZ> extract;int i = 0, nr_points = (int) cloud_filtered->points.size ();// While 30% of the original cloud is still therewhile (cloud_filtered->points.size () > 0.3 * nr_points){// Segment the largest planar component from the remaining cloudseg.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 inliersextract.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 objectextract.setNegative (true);extract.filter (*cloud_f);cloud_filtered.swap (cloud_f);i++;}return (0);

代碼解釋

首先我們用體元柵格濾波器來對數據進行降低采樣。在這里，更少的點意味著花費更少的時間進行計算。

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

下一個代碼塊將處理參數分割。

pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());// Create the segmentation objectpcl::SACSegmentation<pcl::PointXYZ> seg;// Optionalseg.setOptimizeCoefficients (true);// Mandatoryseg.setModelType (pcl::SACMODEL_PLANE);seg.setMethodType (pcl::SAC_RANSAC);seg.setMaxIterations (1000);seg.setDistanceThreshold (0.01);

下面這行

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

和

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

代表了導出的濾波器后的真實的下標。為了處理多個模型，我們把這個教程在一個循環中進行處理，對于每一個被導出的模型，我們返回去獲取指定的點，并且進行迭代，inliers(正常的好的點云)這個將在分割處理后獲取。

運行結果

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.

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