使用VoxelGrid filter对点云进行下采样VoxelGrid可以堪称是个小的3D小方盒 所有存在的点将以它们的质心近似如下采样。这种方法比使用体素的中心近似慢但它更准确地表示底层表面。#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 data pcl::PCDReader reader; // Replace the path below with the path where you saved your file reader.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 object pcl::VoxelGridpcl::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); }这里就只写一次CMakeLists.txt,其他的使用PCL都是类似的cmake_minimum_required(VERSION 2.8 FATAL_ERROR) project(voxelgrid_filter) //add PCL dependency find_package(PCL 1.7 REQUIRED) include_directories(${PCL_INCLUDE_DIRS}) link_directories(${PCL_LIBRARY_DIRS}) add_definitions(${PCL_DEFINITION}) add_executable(voxelgrid_filter main.cpp) target_link_libraries(voxelgrid_filter ${PCL_LIBRARIES}) install(TARGETS voxelgrid_filter RUNTIME DESTINATION bin)看到点云的数量几乎变成原来的十分之一。。。。可以看到橙色是原来采样之前的点是比较密集的蓝色的是采样后的点就变得相对来说更加稀疏了如果继续修改LeafSize还可以进行更加稀疏的采样当过于稀疏会使得点云信息减少对后续的操作不利。
使用VoxelGrid filter对点云进行下采样VoxelGrid可以堪称是个小的3D小方盒 所有存在的点将以它们的质心近似如下采样。这种方法比使用体素的中心近似慢但它更准确地表示底层表面。#include iostream #include pcl/io/pcd_io.h #include pcl/point_types.h #include pcl/filters/voxel_grid.h int main (int argc, char** argv) { pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2 ()); pcl::PCLPointCloud2::Ptr cloud_filtered (new pcl::PCLPointCloud2 ()); // Fill in the cloud data pcl::PCDReader reader; // Replace the path below with the path where you saved your file reader.read (table_scene_lms400.pcd, *cloud); // Remember to download the file first! std::cerr PointCloud before filtering: cloud-width * cloud-height data points ( pcl::getFieldsList (*cloud) ).; // Create the filtering object pcl::VoxelGridpcl::PCLPointCloud2 sor; sor.setInputCloud (cloud); sor.setLeafSize (0.01f, 0.01f, 0.01f); sor.filter (*cloud_filtered); std::cerr PointCloud after filtering: cloud_filtered-width * cloud_filtered-height data points ( pcl::getFieldsList (*cloud_filtered) ).; pcl::PCDWriter writer; writer.write (table_scene_lms400_downsampled.pcd, *cloud_filtered, Eigen::Vector4f::Zero (), Eigen::Quaternionf::Identity (), false); return (0); }这里就只写一次CMakeLists.txt,其他的使用PCL都是类似的cmake_minimum_required(VERSION 2.8 FATAL_ERROR) project(voxelgrid_filter) //add PCL dependency find_package(PCL 1.7 REQUIRED) include_directories(${PCL_INCLUDE_DIRS}) link_directories(${PCL_LIBRARY_DIRS}) add_definitions(${PCL_DEFINITION}) add_executable(voxelgrid_filter main.cpp) target_link_libraries(voxelgrid_filter ${PCL_LIBRARIES}) install(TARGETS voxelgrid_filter RUNTIME DESTINATION bin)看到点云的数量几乎变成原来的十分之一。。。。可以看到橙色是原来采样之前的点是比较密集的蓝色的是采样后的点就变得相对来说更加稀疏了如果继续修改LeafSize还可以进行更加稀疏的采样当过于稀疏会使得点云信息减少对后续的操作不利。
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