我們這次用voxel filter(體元濾波器)來濾波

#include <iostream> #include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/filters/voxel_grid.h>int main (int argc, char** argv) {pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2 ());pcl::PCLPointCloud2::Ptr cloud_filtered (new pcl::PCLPointCloud2 ());// Fill in the cloud datapcl::PCDReader reader;// Replace the path below with the path where you saved your filereader.read ("table_scene_lms400.pcd", *cloud); // Remember to download the file first!std::cerr << "PointCloud before filtering: " << cloud->width * cloud->height << " data points (" << pcl::getFieldsList (*cloud) << ").";// Create the filtering objectpcl::VoxelGrid<pcl::PCLPointCloud2> sor;sor.setInputCloud (cloud);sor.setLeafSize (0.01f, 0.01f, 0.01f);sor.filter (*cloud_filtered);std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height << " data points (" << pcl::getFieldsList (*cloud_filtered) << ").";pcl::PCDWriter writer;writer.write ("table_scene_lms400_downsampled.pcd", *cloud_filtered, Eigen::Vector4f::Zero (), Eigen::Quaternionf::Identity (), false);return (0); }

以下是一些解釋

從磁盤中讀取文件

// Fill in the cloud datapcl::PCDReader reader;// Replace the path below with the path where you saved your filereader.read ("table_scene_lms400.pcd", *cloud); // Remember to download the file first!

然后我們用了VoxelGrid這個濾波器，過濾的尺寸為1cm

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

最終把數據存到磁盤里面

pcl::PCDWriter writer;writer.write ("table_scene_lms400_downsampled.pcd", *cloud_filtered, Eigen::Vector4f::Zero (), Eigen::Quaternionf::Identity (), false);

運行結果，可以看到這把計算量降低到原來的大約十分之一

PointCloud before filtering: 460400 data points (x y z intensity distance sid). PointCloud after filtering: 41049 data points (x y z intensity distance sid).

總結

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