原文链接&＃xff1a;Cylinder model segmentation — Point Cloud Library 0.0 documentation

目录

处理流程

圆柱分割

程序代码

 实验结果

 打印结果

 原始点云

 过滤杂点

 平面分割​

 将平面去除​

 分割圆柱​

CMakeLists.txt

 本教程演示了如何运行圆柱形模型的Sample Consensus分割。为了使示例更实用一些&＃xff0c;将以下操作应用于输入数据集(按顺序)&＃xff1a;

• 距离1.5米以上的数据点被过滤
• 每一点的表面法线被估计
• 一个平面模型被分割&＃xff0c;显示&＃xff0c;并保存到本地
• 一个圆柱形模型(在本数据集中是一个杯子)被分割&＃xff0c;显示&＃xff0c;并保存到本地

本次使用的数据集&＃xff1a;table_scene_mug_stereo_textured.pcd

•  一个杯子
• 一个平面
• 离得很远的背景杂点

由于数据中存在噪声&＃xff0c;圆柱模型并不完美。

处理流程

1. 滤波去噪:pass_through_filter();
2. 法线估计 normals_estimate();
3. 把平面分割出来 plane_seg();
4. 利用分割结果&＃xff08;获取到的下标&＃xff09;&＃xff1a;使用平面点云的下标将平面抽取出来&＃xff0c;并保存&＃xff1a;get_plane();
5. 移除平面及其法线&＃xff0c;将结果保存在cloud_filtered2&＃xff0c;cloud_normals2&＃xff1a;remove_plane();
6. 将圆柱分割出来&＃xff0c;得到系数因子和下标 cylinder_seg();
7. 将圆柱抽取并保存 get_cylinder();

圆柱分割

平面分割可以参考博文&＃xff1a;PCL教程-点云分割之平面模型分割 

seg.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);

• Using a model of type: SACMODEL_NORMAL_PLANE
• Setting normal distance weight to 0.100000
• Using a method of type: SAC_RANSAC with a model threshold of 0.030000
• Setting the maximum number of iterations to 100

下面重点介绍圆柱分割&＃xff1a;

// 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);

•  这里同样使用了RANSAC鲁棒估计来获取圆柱的系数因子。
• 还设置了距离阈值为0.05m(5cm):小于这个阈值的点将标记为圆柱内部点。
• 此外还设置了表面法线的距离权重为0.1
• 限制了圆柱模型的半径为0~0.1m&＃xff08;10cm&＃xff09;
• 最大的迭代次数为10000

程序代码

#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#includetypedef pcl::PointXYZ PointT;
// All the objects needed
pcl::PCDReader reader;
pcl::PassThrough pass;
pcl::NormalEstimation ne;
pcl::SACSegmentationFromNormals seg;
pcl::PCDWriter writer;
pcl::ExtractIndices extract;
pcl::ExtractIndices extract_normals;
pcl::search::KdTree::Ptr tree(new pcl::search::KdTree());// Datasets
pcl::PointCloud::Ptr cloud(new pcl::PointCloud);
pcl::PointCloud::Ptr cloud_filtered(new pcl::PointCloud);
pcl::PointCloud::Ptr cloud_normals(new pcl::PointCloud);
pcl::PointCloud::Ptr cloud_filtered2(new pcl::PointCloud);
pcl::PointCloud::Ptr cloud_normals2(new pcl::PointCloud);
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);pcl::PointCloud::Ptr cloud_plane(new pcl::PointCloud());
pcl::PointCloud::Ptr cloud_cylinder(new pcl::PointCloud());
//PCLVisualizer
pcl::visualization::PCLVisualizer viewer("Cylinder Segmentation");int step_count;void pass_through_filter()
{// Build a passthrough filter to remove spurious NaNspass.setInputCloud(cloud);pass.setFilterFieldName("z");pass.setFilterLimits(0, 1.5);pass.filter(*cloud_filtered);std::cerr <<"PointCloud after filtering has: " <points.size() <<" data points." <{// Estimate point normalsne.setSearchMethod(tree);ne.setInputCloud(cloud_filtered);ne.setKSearch(50);ne.compute(*cloud_normals);
}void plane_seg()
{// 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 <}void get_plane()
{// Extract the planar inliers from the input cloudextract.setInputCloud(cloud_filtered);extract.setIndices(inliers_plane);extract.setNegative(false);// Write the planar inliers to diskextract.filter(*cloud_plane);std::cerr <<"PointCloud representing the planar component: " <points.size() <<" data points." <}void remove_plane()
{// 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);
}void cylinder_seg()
{// 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 <}
void get_cylinder()
{extract.setInputCloud(cloud_filtered2);extract.setIndices(inliers_cylinder);extract.setNegative(false);extract.filter(*cloud_cylinder);if (cloud_cylinder->points.empty())std::cerr <<"Can&＃39;t find the cylindrical component." <points.size() <<" data points." <}void keyboard_event_occurred(const pcl::visualization::KeyboardEvent& event,void * nothing)
{if (event.getKeySym() &＃61;&＃61; "space" && event.keyDown()){&＃43;&＃43;step_count;}
}
int
main(int argc, char** argv)
{step_count &＃61; 1;std::string str &＃61; "STEP";//初始化PCLVisualizer//viewer.addCoordinateSystem(1);//viewer.setBackgroundColor(0, 0, 255);viewer.addText(str&＃43; std::to_string(step_count), 10, 10,16, 200,200,100,"text");viewer.registerKeyboardCallback(&keyboard_event_occurred, (void*)NULL);// Read in the cloud datareader.read("table_scene_mug_stereo_textured.pcd", *cloud);std::cerr <<"PointCloud has: " <points.size() <<" data points." < color_cloud(cloud, 255, 0, 0);viewer.addPointCloud(cloud,"cloud");viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1,"cloud");//滤波去噪pass_through_filter();// 法线估计normals_estimate();// 把平面分割出来plane_seg();// 利用平面点云的下标将平面抽取出来&＃xff0c;并保存get_plane();// 移除平面及其法线&＃xff0c;将结果保存在cloud_filtered2&＃xff0c;cloud_normals2remove_plane();// 将圆柱分割出来&＃xff0c;得到系数因子和下标cylinder_seg();// 将圆柱抽取并保存get_cylinder();while (!viewer.wasStopped()){viewer.spinOnce();if (step_count <8){viewer.updateText(str &＃43; std::to_string(step_count), 10, 10, 16, 50, 100, 200, "text");switch (step_count){case 2://滤波去噪 viewer.updatePointCloud(cloud_filtered, "cloud"); break;case 3://法线估计plane_seg();break;case 4://利用平面点云的下标将平面抽取出来&＃xff0c;并保存viewer.updatePointCloud(cloud_plane, "cloud"); break;case 5://移除平面及其法线&＃xff0c;将结果保存在cloud_filtered2&＃xff0c;cloud_normals2viewer.updatePointCloud(cloud_filtered2, "cloud"); break;case 6:// 将圆柱分割出来&＃xff0c;得到系数因子和下标cylinder_seg(); break;case 7:将圆柱抽取并保存viewer.updatePointCloud(cloud_cylinder, "cloud"); break;default:break;}}else {break;}}return (0);
}


 实验结果

 打印结果

PointCloud has: 307200 data points.
PointCloud after filtering has: 139897 data points.
Plane coefficients: header:
seq: 0 stamp: 0 frame_id:
values[]
  values[0]:   0.0161902
  values[1]:   -0.837667
  values[2]:   -0.545941
  values[3]:   0.528862

PointCloud representing the planar component: 116300 data points.
Cylinder coefficients: header:
seq: 0 stamp: 0 frame_id:
values[]
  values[0]:   0.0543319
  values[1]:   0.100139
  values[2]:   0.787577
  values[3]:   -0.0135876
  values[4]:   0.834831
  values[5]:   0.550338
  values[6]:   0.0387446

PointCloud representing the cylindrical component: 11462 data points.

 原始点云

 过滤杂点

 平面分割

 将平面去除

 分割圆柱

CMakeLists.txt

cmake_minimum_required(VERSION 2.8 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})