#include <iostream>
#include <pcl/console/parse.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/sample_consensus/sac_model_sphere.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <boost/thread/thread.hpp>
boost::shared_ptr<pcl::visualization::PCLVisualizer>
simpleVis (pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{
// --------------------------------------------
// -----Open 3D viewer and add point cloud-----
// --------------------------------------------
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
viewer->setBackgroundColor (0, 0, 0);
viewer->addPointCloud<pcl::PointXYZ> (cloud, "sample cloud");
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");
//viewer->addCoordinateSystem (1.0);
viewer->initCameraParameters ();
return (viewer);
}
int
main(int argc, char** argv)
{
srand(time(NULL));
// initialize PointClouds
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr final (new pcl::PointCloud<pcl::PointXYZ>);
// populate our PointCloud with points
cloud->width = 5000;
cloud->height = 1; //设置无序点云
cloud->is_dense = false;
cloud->points.resize (cloud->width * cloud->height);
for (size_t i = 0; i < cloud->points.size (); ++i)
{
if (pcl::console::find_argument (argc, argv, "-s") >= 0 || pcl::console::find_argument (argc, argv, "-sf") >= 0)
{
cloud->points[i].x = rand () / (RAND_MAX + 1.0);
cloud->points[i].y = rand () / (RAND_MAX + 1.0);
if (i % 5 == 0)
cloud->points[i].z = rand () / (RAND_MAX + 1.0);
else if(i % 2 == 0)
cloud->points[i].z = sqrt( 1 - (cloud->points[i].x * cloud->points[i].x)
- (cloud->points[i].y * cloud->points[i].y));
else
cloud->points[i].z = - sqrt( 1 - (cloud->points[i].x * cloud->points[i].x)
- (cloud->points[i].y * cloud->points[i].y));
}
else
{
cloud->points[i].x = rand () / (RAND_MAX + 1.0);
cloud->points[i].y = rand () / (RAND_MAX + 1.0);
if( i % 5 == 0)
cloud->points[i].z = rand () / (RAND_MAX + 1.0); //此处对应点为局外点
else
cloud->points[i].z = -1 * (cloud->points[i].x + cloud->points[i].y);
}
}
std::vector<int> inliers; //储存局内点集合的点的索引的向量
// created RandomSampleConsensus object and compute the appropriated model
pcl::SampleConsensusModelSphere<pcl::PointXYZ>::Ptr
model_s(new pcl::SampleConsensusModelSphere<pcl::PointXYZ> (cloud));
//针对于球模型的对象
pcl::SampleConsensusModelPlane<pcl::PointXYZ>::Ptr
model_p (new pcl::SampleConsensusModelPlane<pcl::PointXYZ> (cloud));
//针对于平面模型对象
//下面是本文重点
if(pcl::console::find_argument (argc, argv, "-f") >= 0)
{
pcl::RandomSampleConsensus<pcl::PointXYZ> ransac (model_p);
ransac.setDistanceThreshold (.01);//与平面距离小于0.01的点作为局内点考虑
ransac.computeModel(); //执行随机参数估计
ransac.getInliers(inliers); //储存估计所得的局内点
}
else if (pcl::console::find_argument (argc, argv, "-sf") >= 0 )
{
pcl::RandomSampleConsensus<pcl::PointXYZ> ransac (model_s);
ransac.setDistanceThreshold (.01);
ransac.computeModel();
ransac.getInliers(inliers);
}
// copies all inliers of the model computed to another PointCloud
//复制估算模型的所有局内点到final中
pcl::copyPointCloud<pcl::PointXYZ>(*cloud, inliers, *final);
// creates the visualization object and adds either our orignial cloud or all of the inliers
// depending on the command line arguments specified.
//创建可视化对象
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer;
if (pcl::console::find_argument (argc, argv, "-f") >= 0 || pcl::console::find_argument (argc, argv, "-sf") >= 0)
viewer = simpleVis(final);
else
viewer = simpleVis(cloud);
while (!viewer->wasStopped ())
{
viewer->spinOnce (100);
boost::this_thread::sleep (boost::posix_time::microseconds (100000));
}
return 0;
}
点云库PCL学习——如何使用随机采样一致性模型
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转载自blog.csdn.net/zzh_AI/article/details/93196545
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