用一个参数化的模型来投影点

这次我们将学着怎么通过一个参数化的模型进行投影。这个参数化的模型是通过一系列的系数---在这里是平面，相当于ax+by+cz+d=0

下面是代码

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/ModelCoefficients.h>
#include <pcl/filters/project_inliers.h>
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>);
// Fill in the cloud data
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;
// Create a set of planar coefficients with X=Y=0,Z=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;
// Create the filtering object
pcl::ProjectInliers<pcl::PointXYZ> proj;
proj.setModelType (pcl::SACMODEL_PLANE);
proj.setInputCloud (cloud);
proj.setModelCoefficients (coefficients);
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;
return (0);
}

以下是一些解释

产生随机点云

  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;

接下去，我们设置了一些参数，然后实现了一个平面

  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;

接下去，我们创建了ProjectInliers这个对象，并使用ModelCoefficients定义了上面的模型

  pcl::ProjectInliers<pcl::PointXYZ> proj;
proj.setModelType (pcl::SACMODEL_PLANE);
proj.setInputCloud (cloud);
proj.setModelCoefficients (coefficients);
proj.filter (*cloud_projected);

最终，我们将展示投影点云的内容

  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;

我们运行程序将得到以下的结果

Cloud before projection:
0.352222 -0.151883 -0.106395
-0.397406 -0.473106 0.292602
-0.731898 0.667105 0.441304
-0.734766 0.854581 -0.0361733
-0.4607 -0.277468 -0.916762
Cloud after projection:
0.352222 -0.151883 0
-0.397406 -0.473106 0
-0.731898 0.667105 0
-0.734766 0.854581 0
-0.4607 -0.277468 0