PCL教程-点云分割之圆柱体分割

Love The Way You Lie 2022-09-10 11:13 386阅读 0赞

> 原文链接：[https://pcl.readthedocs.io/projects/tutorials/en/latest/cylinder\_segmentation.html\#cylinder-segmentation][https_pcl.readthedocs.io_projects_tutorials_en_latest_cylinder_segmentation.html_cylinder-segmentation]

**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，将以下操作应用于输入数据集(按顺序)：

*  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，显示，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，显示，并保存到本地

本次使用的数据集：[table\_scene\_mug\_stereo\_textured.pcd][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，圆柱模型并不完美。

![20210908101629635.png][]

![20210908101706167.png][]

# 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（获取到的下标）：使用平面点云的下标将平面抽取出来，并保存：get\_plane();
5.  移除平面及其法线，将结果保存在cloud\_filtered2，cloud\_normals2：remove\_plane();
6.  将圆柱分割出来，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：[PCL教程-点云分割之平面模型分割][PCL_-]

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：

// Create the segmentation object for cylinder segmentation and set all the parameters
      seg.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（10cm）
>  *  最大的迭代次数为10000

# 程序代码 #

#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>
    #include<pcl/visualization/pcl_visualizer.h>
    #include<string>
    
    typedef pcl::PointXYZ PointT;
    // All the objects needed
    pcl::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>());
    
    // Datasets
    pcl::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);
    
    pcl::PointCloud<PointT>::Ptr cloud_plane(new pcl::PointCloud<PointT>());
    pcl::PointCloud<PointT>::Ptr cloud_cylinder(new pcl::PointCloud<PointT>());
    //PCLVisualizer
    pcl::visualization::PCLVisualizer viewer("Cylinder Segmentation");
    
    int step_count;
    
    void pass_through_filter()
    {
    	// Build a passthrough filter to remove spurious NaNs
    	pass.setInputCloud(cloud);
    	pass.setFilterFieldName("z");
    	pass.setFilterLimits(0, 1.5);
    	pass.filter(*cloud_filtered);
    	std::cerr << "PointCloud after filtering has: " << cloud_filtered->points.size() << " data points." << std::endl;
    
    }
    
    void normals_estimate()
    {
    	// Estimate point normals
    	ne.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 parameters
    	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);
    	// Obtain the plane inliers and coefficients
    	seg.segment(*inliers_plane, *coefficients_plane);
    	std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;
    }
    
    void get_plane()
    {
    	// Extract the planar inliers from the input cloud
    	extract.setInputCloud(cloud_filtered);
    	extract.setIndices(inliers_plane);
    	extract.setNegative(false);
    
    	// Write the planar inliers to disk
    	
    	extract.filter(*cloud_plane);
    	std::cerr << "PointCloud representing the planar component: " << cloud_plane->points.size() << " data points." << std::endl;
    	writer.write("table_scene_mug_stereo_textured_plane.pcd", *cloud_plane, false);
    }
    
    void remove_plane()
    {
    	// Remove the planar inliers, extract the rest
    	extract.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 parameters
    	seg.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 coefficients
    	seg.segment(*inliers_cylinder, *coefficients_cylinder);
    	std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;
    }
    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't find the cylindrical component." << std::endl;
    	else
    	{
    		std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->points.size() << " data points." << std::endl;
    		writer.write("table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false);
    	}
    }
    
    void keyboard_event_occurred(const pcl::visualization::KeyboardEvent& event,
    	void * nothing)
    {
    	if (event.getKeySym() == "space" && event.keyDown())
    	{
    		++step_count;
    	}
    }
    int
    main(int argc, char** argv)
    {
    	step_count = 1;
    	std::string str = "STEP";
    	//初始化PCLVisualizer
    	//viewer.addCoordinateSystem(1);
    	//viewer.setBackgroundColor(0, 0, 255);
    	viewer.addText(str+ std::to_string(step_count), 10, 10,16, 200,200,100,"text");
    	
    	viewer.registerKeyboardCallback(&keyboard_event_occurred, (void*)NULL);
    
    
    	// Read in the cloud data
    	reader.read("table_scene_mug_stereo_textured.pcd", *cloud);
    	std::cerr << "PointCloud has: " << cloud->points.size() << " data points." << std::endl;
    
    	// 显示原始点云
    	//pcl::visualization::PointCloudColorHandlerCustom<PointT> 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，并保存
    	get_plane();
    
    	// 移除平面及其法线，将结果保存在cloud_filtered2，cloud_normals2
    	remove_plane();
    
    	// 将圆柱分割出来，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 + 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，并保存
    				viewer.updatePointCloud(cloud_plane, "cloud"); break;
    			case 5:
    				//移除平面及其法线，将结果保存在cloud_filtered2，cloud_normals2
    				viewer.updatePointCloud(cloud_filtered2, "cloud"); break;
    			case 6:
    				// 将圆柱分割出来，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 ##

![2021090717404816.png][]

## 过滤杂点 ##

![20210907174104289.png][]

![20210907174121707.png][]

## 平面分割![2021090717413595.png][] ##

## 将平面去除![20210907174152386.png][] ##

![20210907174207822.png][]

## 分割圆柱![20210907174222164.png][] ##

![20210907174500576.png][]

# 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})

[https_pcl.readthedocs.io_projects_tutorials_en_latest_cylinder_segmentation.html_cylinder-segmentation]: https://pcl.readthedocs.io/projects/tutorials/en/latest/cylinder_segmentation.html#cylinder-segmentation
[table_scene_mug_stereo_textured.pcd]: https://raw.github.com/PointCloudLibrary/data/master/tutorials/table_scene_mug_stereo_textured.pcd
[20210908101629635.png]: /images/20220829/0090f5544a3342c58354535462286dd7.png
[20210908101706167.png]: /images/20220829/168e5b48d12448b2a92c11ed27106832.png
[PCL_-]: https://blog.csdn.net/luolaihua2018/article/details/120136665
[2021090717404816.png]: /images/20220829/fe4a5abab4fc41a49abf83cae5d71dc7.png
[20210907174104289.png]: /images/20220829/a4fc7126934249438d0461f428c4ec95.png
[20210907174121707.png]: /images/20220829/42a6e9d38aac4890b7920c1cba83c198.png
[2021090717413595.png]: /images/20220829/37c1e1856fec4cc7af8f8cd99f75b2a1.png
[20210907174152386.png]: /images/20220829/97c9b4cc99944f838e1bee6049010a0d.png
[20210907174207822.png]: /images/20220829/edf2fd4af1bc4a48a48f89d52760116a.png
[20210907174222164.png]: /images/20220829/4e8455cc678b4c3f9a9d3dceaa8ae1fd.png
[20210907174500576.png]: /images/20220829/98c680d5d9c043ec9d36590eb4428a07.png