002：pcl 点云投影

概述1.首先包含的对应的ModelCoefficients.h以及filter中向平面投影的project_inlier.h #include <iostream>#include <pcl/io/pcd_io.h>#include <pcl/point_types.h>#include <pcl/ModelCoefficients.h>#include <pcl/filters/pr

1.首先包含的对应的ModelCoefficIEnts.h以及filter中向平面投影的project_inlIEr.h@H_419_9@

#include <iostream>#include <pcl/io/pcd_io.h>#include <pcl/point_types.h>#include <pcl/ModelCoefficIEnts.h>#include <pcl/filters/project_inlIErs.h>

2.创建点云对象指针并初始化，输出到屏幕@H_419_9@

/2.初始化该对象? cloud->wIDth? = 5;//对于未组织的点云的相当于points个数? cloud->height = 1; //对未组织的点云指定为1? cloud->points.resize (cloud->wIDth * cloud->height); //修剪或追加值初始化的元素? 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);? }? // 3.cerr 输出对象放置刷屏? 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;

//投影前点`Cloud before projection:    1.28125 577.094 197.938    828.125 599.031 491.375    358.688 917.438 842.563    764.5 178.281 879.531    727.531 525.844 311.281

3.设置ModelCoefficIEnts值。在这种情况下，我们使用一个平面模型，其中ax + by + cz + d = 0，其中a = b = d = 0，c = 1，或者换句话说，XY平面@H_419_9@

? // 4.创建一个系数为X=Y=0,Z=1的平面? 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;

4.通过该滤波将所有的点投影到创建的平面上，并输出结果
** 注意这里在使用的时候再创建滤波后对象不规范，应该放在程序开始的时候**@H_419_9@

? //5.创建滤波后对象，并通过滤波投影，并显示结果? pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected(new pcl::PointCloud<pcl::PointXYZ>);? // 创建滤波器对象? 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);

//投影后点Cloud before projection:    1.28125 577.094 197.938    828.125 599.031 491.375    358.688 917.438 842.563    764.5 178.281 879.531    727.531 525.844 311.281Cloud 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

6.参考网址
pcl官网例程
all-in_one 中的有API 以及例子，但是具体理论说明还是参考官网吧！
...\PCL-1.8.1-AllinOne-msvc2017-win64(1)\share\doc\pcl-1.8\tutorials\sources中 例子要比pcl入门精通要全@H_419_9@ 总结

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