3d激光雷达开发（圆柱分割）

和平面分割一样，pcl也支持圆柱分割。使用的方法和平面分割也差不多，都是基于ransac的基本原理。在pcl官方库当中，也给出了参考代码，注意关联的pcd文件，https://pcl.readthedocs.io/projects/tutorials/en/master/cylinder_segmentation.html#cylinder-segmentation

1、准备cylinder_segmentation.cpp文件

#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/passthrough.h>
#include <pcl/features/normal_3d.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>typedef pcl::PointXYZ PointT;int
main ()
{// All the objects neededpcl::PCDReader reader;pcl::PassThrough<PointT> pass;pcl::NormalEstimation<PointT, pcl::Normal> ne;pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg; pcl::PCDWriter writer;pcl::ExtractIndices<PointT> extract;pcl::ExtractIndices<pcl::Normal> extract_normals;pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ());// Datasetspcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>);pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<PointT>);pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_cylinder (new pcl::ModelCoefficients);pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_cylinder (new pcl::PointIndices);// Read in the cloud datareader.read ("table_scene_mug_stereo_textured.pcd", *cloud);std::cerr << "PointCloud has: " << cloud->size () << " data points." << std::endl;// Build a passthrough filter to remove spurious NaNs and scene backgroundpass.setInputCloud (cloud);pass.setFilterFieldName ("z");pass.setFilterLimits (0, 1.5);pass.filter (*cloud_filtered);std::cerr << "PointCloud after filtering has: " << cloud_filtered->size () << " data points." << std::endl;// Estimate point normalsne.setSearchMethod (tree);ne.setInputCloud (cloud_filtered);ne.setKSearch (50);ne.compute (*cloud_normals);// Create the segmentation object for the planar model and set all the parametersseg.setOptimizeCoefficients (true);seg.setModelType (pcl::SACMODEL_NORMAL_PLANE);seg.setNormalDistanceWeight (0.1);seg.setMethodType (pcl::SAC_RANSAC);seg.setMaxIterations (100);seg.setDistanceThreshold (0.03);seg.setInputCloud (cloud_filtered);seg.setInputNormals (cloud_normals);// Obtain the plane inliers and coefficientsseg.segment (*inliers_plane, *coefficients_plane);std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;// Extract the planar inliers from the input cloudextract.setInputCloud (cloud_filtered);extract.setIndices (inliers_plane);extract.setNegative (false);// Write the planar inliers to diskpcl::PointCloud<PointT>::Ptr cloud_plane (new pcl::PointCloud<PointT> ());extract.filter (*cloud_plane);std::cerr << "PointCloud representing the planar component: " << cloud_plane->size () << " data points." << std::endl;writer.write ("table_scene_mug_stereo_textured_plane.pcd", *cloud_plane, false);// Remove the planar inliers, extract the restextract.setNegative (true);extract.filter (*cloud_filtered2);extract_normals.setNegative (true);extract_normals.setInputCloud (cloud_normals);extract_normals.setIndices (inliers_plane);extract_normals.filter (*cloud_normals2);// Create the segmentation object for cylinder segmentation and set all the parametersseg.setOptimizeCoefficients (true);seg.setModelType (pcl::SACMODEL_CYLINDER);seg.setMethodType (pcl::SAC_RANSAC);seg.setNormalDistanceWeight (0.1);seg.setMaxIterations (10000);seg.setDistanceThreshold (0.05);seg.setRadiusLimits (0, 0.1);seg.setInputCloud (cloud_filtered2);seg.setInputNormals (cloud_normals2);// Obtain the cylinder inliers and coefficientsseg.segment (*inliers_cylinder, *coefficients_cylinder);std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;// Write the cylinder inliers to diskextract.setInputCloud (cloud_filtered2);extract.setIndices (inliers_cylinder);extract.setNegative (false);pcl::PointCloud<PointT>::Ptr cloud_cylinder (new pcl::PointCloud<PointT> ());extract.filter (*cloud_cylinder);if (cloud_cylinder->points.empty ()) std::cerr << "Can't find the cylindrical component." << std::endl;else{std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->size () << " data points." << std::endl;writer.write ("table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false);}return (0);
}

2、代码分析

代码的整个流程大体可以分成两个部分，第一个部分是提取平面，第二个部分是提取圆柱。

3、准备CMakeLists.txt

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)project(cylinder_segmentation)find_package(PCL 1.2 REQUIRED)include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})add_executable (cylinder_segmentation cylinder_segmentation.cpp)
target_link_libraries (cylinder_segmentation ${PCL_LIBRARIES})

4、生成sln工程，准备编译

5、运行exe文件，注意差别

3d激光雷达开发（圆柱分割）

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