PCL 提取点云边界轮廓-AC方法、平面轮廓_点云轮廓提取

一、概述

PCL点云边界特征检测 （附完整代码 C++）_pcl计算点云特征值_McQueen_LT的博客-CSDN博客

在点云的边界特征检测（网格模型的边界特征检测已经是一个确定性问题了，见 网格模型边界检测）方面，PCL中有一个针对点云边界的可以称作为是s t a t e − o f − t h e − a r t state-of-the-artstate−of−the−art的方法，这个方法出自 D e t e c t i n g   H o l e s   i n   P o i n t   S e t   S u r f a c e s Detecting\space Holes\space in\space Point\space Set\space SurfacesDetecting Holes in Point Set Surfaces这篇论文，叫做 A n g l e   C r i t e r i o n Angle\ CriterionAngle Criterion，简称 A C ACAC。这篇论文中还提出了另一种检测边界的方法是H a l f d i s c   C r i t e r i o n Halfdisc\ CriterionHalfdisc Criterion，H d C HdCHdC。目前PCL中应该只集成了A C ACAC，因为这个方法确实比H d C HdCHdC好，已经够用了。这两种方法的思路都非常简单，但是却非常有效，而往往流传下来的经典方法都是这种简单有效的方法。

二、AC方法步骤讲解

 

三、AC方法代码

原始点云：

计算步骤

1、计算点云的法向量

2、计算点云的边界

3、显示

代码

 
int PointCloudBoundary2(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud)
{
 
	// 1 计算法向量
	pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
	pcl::PointCloud<pcl::Normal>::Ptr  normals(new  pcl::PointCloud<pcl::Normal>);
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setSearchMethod(tree);
	normalEstimation.setRadiusSearch(0.02);  // 法向量的半径
	normalEstimation.compute(*normals);
 
	/*pcl计算边界*/
	pcl::PointCloud<pcl::Boundary>::Ptr boundaries(new pcl::PointCloud<pcl::Boundary>); //声明一个boundary类指针，作为返回值
	boundaries->resize(cloud->size()); //初始化大小
	pcl::BoundaryEstimation<pcl::PointXYZ, pcl::Normal, pcl::Boundary> boundary_estimation; //声明一个BoundaryEstimation类
	boundary_estimation.setInputCloud(cloud); //设置输入点云
	boundary_estimation.setInputNormals(normals); //设置输入法线
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree_ptr(new pcl::search::KdTree<pcl::PointXYZ>);
	boundary_estimation.setSearchMethod(kdtree_ptr); //设置搜寻k近邻的方式
	boundary_estimation.setKSearch(30); //设置k近邻数量
	boundary_estimation.setAngleThreshold(M_PI * 0.6); //设置角度阈值，大于阈值为边界
	boundary_estimation.compute(*boundaries); //计算点云边界，结果保存在boundaries中
	
	cout << "边界点云的点数   ：  "<< boundaries->size()<< endl;
 
 
	/*可视化*/
	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_visual(new pcl::PointCloud<pcl::PointXYZRGB>);
	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_boundary(new pcl::PointCloud<pcl::PointXYZRGB>);
	cloud_visual->resize(cloud->size());
	for (size_t i = 0; i < cloud->size(); i++)
	{
		cloud_visual->points[i].x = cloud->points[i].x;
		cloud_visual->points[i].y = cloud->points[i].y;
		cloud_visual->points[i].z = cloud->points[i].z;
		if (boundaries->points[i].boundary_point != 0)
		{
			cloud_visual->points[i].r = 255;
			cloud_visual->points[i].g = 0;
			cloud_visual->points[i].b = 0;
			cloud_boundary->push_back(cloud_visual->points[i]);
		}
		else
		{
			cloud_visual->points[i].r = 255;
			cloud_visual->points[i].g = 255;
			cloud_visual->points[i].b = 255;
		}
	}
	pcl::io::savePCDFileBinaryCompressed("D:\\work\\Pointclouds\\clouds\\all.pcd", *cloud_visual);
	pcl::io::savePCDFileBinaryCompressed("D:\\work\\Pointclouds\\clouds\\boundaries.pcd", *cloud_boundary);
	return 0;
}

显示

	boundary_estimation.setKSearch(30); //设置k近邻数量
	boundary_estimation.setAngleThreshold(M_PI * 0.6); //设置角度阈值，大于阈值为边界
	boundary_estimation.compute(*boundaries); //计算点云边界，结果保存在boundaries中

M_PI*0.6 

M_PI*0.8

四、平面轮廓

原始点云：

步骤：

1、采用RANSAC提取平面

2、提取平面

代码：

 
int PointCloudBoundary2(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud)
{
 
	// 1 计算法向量
	pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
	pcl::PointCloud<pcl::Normal>::Ptr  normals(new  pcl::PointCloud<pcl::Normal>);
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setSearchMethod(tree);
	normalEstimation.setRadiusSearch(0.02);  // 法向量的半径
	normalEstimation.compute(*normals);
 
	/*pcl计算边界*/
	pcl::PointCloud<pcl::Boundary>::Ptr boundaries(new pcl::PointCloud<pcl::Boundary>); //声明一个boundary类指针，作为返回值
	boundaries->resize(cloud->size()); //初始化大小
	pcl::BoundaryEstimation<pcl::PointXYZ, pcl::Normal, pcl::Boundary> boundary_estimation; //声明一个BoundaryEstimation类
	boundary_estimation.setInputCloud(cloud); //设置输入点云
	boundary_estimation.setInputNormals(normals); //设置输入法线
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree_ptr(new pcl::search::KdTree<pcl::PointXYZ>);
	boundary_estimation.setSearchMethod(kdtree_ptr); //设置搜寻k近邻的方式
	boundary_estimation.setKSearch(30); //设置k近邻数量
	boundary_estimation.setAngleThreshold(M_PI * 0.8); //设置角度阈值，大于阈值为边界
	boundary_estimation.compute(*boundaries); //计算点云边界，结果保存在boundaries中
	
	cout << "边界点云的点数   ：  "<< boundaries->size()<< endl;
 
 
	/*可视化*/
	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_visual(new pcl::PointCloud<pcl::PointXYZRGB>);
	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_boundary(new pcl::PointCloud<pcl::PointXYZRGB>);
	cloud_visual->resize(cloud->size());
	for (size_t i = 0; i < cloud->size(); i++)
	{
		cloud_visual->points[i].x = cloud->points[i].x;
		cloud_visual->points[i].y = cloud->points[i].y;
		cloud_visual->points[i].z = cloud->points[i].z;
		if (boundaries->points[i].boundary_point != 0)
		{
			cloud_visual->points[i].r = 255;
			cloud_visual->points[i].g = 0;
			cloud_visual->points[i].b = 0;
			cloud_boundary->push_back(cloud_visual->points[i]);
		}
		else
		{
			cloud_visual->points[i].r = 255;
			cloud_visual->points[i].g = 255;
			cloud_visual->points[i].b = 255;
		}
	}
	pcl::io::savePCDFileBinaryCompressed("D:\\work\\Pointclouds\\clouds\\all22.pcd", *cloud_visual);
	pcl::io::savePCDFileBinaryCompressed("D:\\work\\Pointclouds\\clouds\\boundaries222.pcd", *cloud_boundary);
	return 0;
}
/// <summary>
///   先提取点云的平面然后在进行边界林廓
/// </summary>
/// <param name="cloud"></param>
/// <returns></returns>
int  PlaneCloudContour(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud)
{
 
	// 0 计算法向量
	pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
	pcl::PointCloud<pcl::Normal>::Ptr  normals(new  pcl::PointCloud<pcl::Normal>);
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setSearchMethod(tree);
	normalEstimation.setRadiusSearch(0.02);  // 法向量的半径
	normalEstimation.compute(*normals);
 
 
	 // 1 提出出平面 
		// 提取平面点云的索引
	pcl::PointIndices::Ptr  index_plane(new pcl::PointIndices);
	pcl::SACSegmentationFromNormals<pcl::PointXYZ, pcl::Normal> sacSegmentationFromNormals;
	pcl::ModelCoefficients::Ptr mdelCoefficients_plane(new pcl::ModelCoefficients);
	sacSegmentationFromNormals.setInputCloud(cloud);
	sacSegmentationFromNormals.setOptimizeCoefficients(true);//设置对估计的模型系数需要进行优化
	sacSegmentationFromNormals.setModelType(pcl::SACMODEL_NORMAL_PLANE); //设置分割模型
	sacSegmentationFromNormals.setNormalDistanceWeight(0.1);//设置表面法线权重系数
	sacSegmentationFromNormals.setMethodType(pcl::SAC_RANSAC);//设置采用RANSAC作为算法的参数估计方法
	sacSegmentationFromNormals.setMaxIterations(500); //设置迭代的最大次数
	sacSegmentationFromNormals.setDistanceThreshold(0.05); //设置内点到模型的距离允许最大值
	sacSegmentationFromNormals.setInputCloud(cloud);
  	sacSegmentationFromNormals.setInputNormals(normals);
	sacSegmentationFromNormals.segment(*index_plane, *mdelCoefficients_plane);
	std::cerr << "Plane coefficients: " << *mdelCoefficients_plane << std::endl;
 
	 // 点云提取
	pcl::ExtractIndices<pcl::PointXYZ> extractIndices;
	pcl::PointCloud<pcl::PointXYZ>::Ptr  cloud_p(new pcl::PointCloud<pcl::PointXYZ>);
	extractIndices.setInputCloud(cloud);
	extractIndices.setIndices(index_plane);
	extractIndices.setNegative(false);
	extractIndices.filter(*cloud_p);
	pcl::io::savePCDFileBinaryCompressed("D:\\work\\Pointclouds\\clouds\\planeCloud.pcd",*cloud_p);
	 // 2  
	PointCloudBoundary2(cloud_p);
	return 0;
}

 结果：

平面点云的轮廓线计算-alpha shapes算法原理和实现_α-shape算法-CSDN博客

双阈值Alpha Shapes算法提取点云建筑物轮廓研究 - 豆丁网