PCL教程-点云滤波之投影滤波

川长思鸟来 2022-08-28 15:56 349阅读 0赞

原文链接：[Projecting points using a parametric model][]

本小节将学习如何将点投影到一个参数化模型上(例如平面或球等)。参数化模型通过一组参数来设定，对于平面来说使用其等式形式**ax+by+cz+d=0**，在PCI中有专门存储常见模型系数的数据结构。

# 程序代码 #

#include <iostream>
    #include <pcl/io/pcd_io.h>
    #include <pcl/point_types.h>
    #include <pcl/ModelCoefficients.h>
    #include <pcl/filters/project_inliers.h>
    #include<pcl/visualization/pcl_visualizer.h>
    int
    main(int argc, char** argv)
    {
    	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected(new pcl::PointCloud<pcl::PointXYZ>);
    
    	pcl::visualization::PCLVisualizer viewer("project inliers");
    	int v1(1);
    	int v2(2);
    	viewer.createViewPort(0, 0, 0.5, 1, v1);
    	viewer.createViewPort(0.5, 0, 1, 1, v2);
    
    
    	// 填入点云数据
    	cloud->width = 5;
    	cloud->height = 1;
    	cloud->points.resize(cloud->width * cloud->height);
    	for (size_t i = 0; i < cloud->points.size(); ++i)
    	{
    		cloud->points[i].x = 1024 * rand() / (RAND_MAX + 1.0f);
    		cloud->points[i].y = 1024 * rand() / (RAND_MAX + 1.0f);
    		cloud->points[i].z = 1024 * rand() / (RAND_MAX + 1.0f);
    	}
    	std::cerr << "Cloud before projection: " << std::endl;
    	for (size_t i = 0; i < cloud->points.size(); ++i)
    		std::cerr << "    " << cloud->points[i].x << " "
    		<< cloud->points[i].y << " "
    		<< cloud->points[i].z << std::endl;
    	// 平面公式：ax + by + cz + d = 0
    	// 创建一个系数为X=Y=0,Z=1的平面,相当于是x-y平面
    	// a = b = d = 0, c = 1;
    	pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients());
    	coefficients->values.resize(4);
    	coefficients->values[0] = coefficients->values[1] = 0;
    	coefficients->values[2] = 1.0;
    	coefficients->values[3] = 0;
    	// 创建滤波器对象
    	pcl::ProjectInliers<pcl::PointXYZ> proj;//创建滤波器对象
    	proj.setModelType(pcl::SACMODEL_PLANE); //设置对象对应的投影模型
    	proj.setInputCloud(cloud);
    	proj.setModelCoefficients(coefficients);//设置投影模型的参数因子
    	proj.filter(*cloud_projected);
    
    	std::cerr << "Cloud after projection: " << std::endl;
    	for (size_t i = 0; i < cloud_projected->points.size(); ++i)
    		std::cerr << "    " << cloud_projected->points[i].x << " "
    		<< cloud_projected->points[i].y << " "
    		<< cloud_projected->points[i].z << std::endl;
    
    	viewer.addPointCloud(cloud, "c1", v1);
    	viewer.addPointCloud(cloud_projected, "c2", v2);
    	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1000, "c1", v1);
    	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1000, "c2", v2);
    	viewer.addCoordinateSystem(1000, cloud_projected->points[0].x, cloud_projected->points[0].y, cloud_projected->points[0].z, v2);
    	viewer.addCoordinateSystem(1000, cloud->points[0].x, cloud->points[0].y, cloud->points[0].z, v1);
    
    	while (!viewer.wasStopped())
    	{
    		viewer.spinOnce();
    	}
    	return (0);
    }

在本程序中使用的投影模型为平面。

> 平面公式：ax + by + cz + d = 0
> 
> 创建一个系数为X=Y=0,Z=1的平面,相当于是x-y平面  
> 系数设置为：a = b = d = 0, c = 1;

// 平面公式：ax + by + cz + d = 0
    	// 创建一个系数为X=Y=0,Z=1的平面,相当于是x-y平面
    	// a = b = d = 0, c = 1;
    	pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients());
    	coefficients->values.resize(4);
    	coefficients->values[0] = coefficients->values[1] = 0;
    	coefficients->values[2] = 1.0;
    	coefficients->values[3] = 0;
    	// 创建滤波器对象
    	pcl::ProjectInliers<pcl::PointXYZ> proj;//创建滤波器对象
    	proj.setModelType(pcl::SACMODEL_PLANE); //设置对象对应的投影模型
    	proj.setInputCloud(cloud);
    	proj.setModelCoefficients(coefficients);//设置投影模型的参数因子
    	proj.filter(*cloud_projected);

# 实验结果 #

首先随机生成五个点，如左图。

然后将所有点全部投影到X-Y平面（Z=1)：如右图：

![20210911153827877.png][]

![20210911153847348.png][] ![20210911153908687.png][]

![20210911154530226.png][]

> Cloud before projection:  
> 1.28125 577.094 197.938  
> 828.125 599.031 491.375  
> 358.688 917.438 842.562  
> 764.5 178.281 879.531  
> 727.531 525.844 311.281  
> Cloud size before projection: 5  
> Cloud after projection:  
> 1.28125 577.094 0  
> 828.125 599.031 0  
> 358.688 917.438 0  
> 764.5 178.281 0  
> 727.531 525.844 0  
> Cloud size after projection: 5

由上述结果可知：将点云投影到一个平面，会使点云的某个坐标失去意义（坐标值相同），将三维的点云变成了二维的图像。

同理，对点云数据 [table\_scene\_lms400.pcd][table_scene_lms400.pcd]进行投影滤波的结果如下：

![2021091116214917.png][]

![20210911162206761.png][]

![20210911162251813.png][]

三维的点云图像已经失去了一个坐标的数据，变成了二维扁平的点云图像。

[Projecting points using a parametric model]: https://pcl.readthedocs.io/projects/tutorials/en/latest/project_inliers.html#project-inliers
[20210911153827877.png]: /images/20220828/b3be2d34cf64458290b059b7c5477ccb.png
[20210911153847348.png]: https://img-blog.csdnimg.cn/20210911153847348.png
[20210911153908687.png]: /images/20220828/667b2021ab5f4efea1e3bbd784456fae.png
[20210911154530226.png]: /images/20220828/49362426a5ea4dcaa8418ec1311cb7a4.png
[table_scene_lms400.pcd]: https://raw.github.com/PointCloudLibrary/data/master/tutorials/table_scene_lms400.pcd
[2021091116214917.png]: /images/20220828/47eda738fd46482bae6ce0d4ce073ba7.png
[20210911162206761.png]: /images/20220828/86ca4648ebc24d6ba38aae795eccefa0.png
[20210911162251813.png]: /images/20220828/a0fa3ef59534424a97590f7feb740b19.png