点云pcl的入门步骤（PCL点云库调库学习系列）
点云pcl的入门步骤（PCL点云库调库学习系列）
随机采样一致性实现功能从输入点云中，找到符合某种模型的点云集
具体为：随机产生一系列点云，创建两个随机采样一致性模型为球和平面，根据参数的不同选择不同的模型，执行参数估计，可以得到模型中的局内点，最后将结果可视化显示出来
关键函数//1.随机采样执行流程
pcl::SampleConsensusModelPlane<pcl::PointXYZ>::Ptr
model_p(new pcl::SampleConsensusModelPlane<pcl::PointXYZ>(cloud)); //1平面模型
pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_p); //2随机采样一致性对象
ransac.setDistanceThreshold(.01); //3设置距离阈值，小于0.01的点作为局内点考虑
ransac.computeModel(); //4执行随机参数估计
ransac.getInliers(inliers); //5获取所得内点，结果保存在inliers中
//复制局内点点云
pcl::copyPointCloud(*cloud inliers *final); //将cloud点云中索引为inliers中所有点复制到final中
//另外一个常用的重载版本的函数
//将输入点云复制到输出点云中
void pcl::copyPointCloud(const pcl::PointCloud<PointInT>& cloud_in //输入点云
pcl::PointCloud<PointOutT>& cloud_out //输出点云
)
完整代码
#include <iostream>
#include <thread>
#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>
using namespace std::chrono_literals;
pcl::visualization::PCLVisualizer::Ptr
simpleVis(pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{
// --------------------------------------------
// -----Open 3D viewer and add point cloud-----
// --------------------------------------------
//点云显示
pcl::visualization::PCLVisualizer::Ptr 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 "global");
viewer->initCameraParameters();
return (viewer);
}
int
main(int argc char** argv)
{
// 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 = 500;
cloud->height = 1;
cloud->is_dense = false;
cloud->points.resize(cloud->width * cloud->height);
//生成点云数据
for (std::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 = 1024 * rand() / (RAND_MAX 1.0);
cloud->points[i].y = 1024 * rand() / (RAND_MAX 1.0);
if (i % 5 == 0)
cloud->points[i].z = 1024 * 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 = 1024 * rand() / (RAND_MAX 1.0);
cloud->points[i].y = 1024 * rand() / (RAND_MAX 1.0);
if (i % 2 == 0)
cloud->points[i].z = 1024 * rand() / (RAND_MAX 1.0);
else
cloud->points[i].z = -1 * (cloud->points[i].x cloud->points[i].y);
}
}
std::vector<int> inliers;
// created RandomSampleConsensus object and compute the appropriated model
//创建随机采样一致性对象，并计算合适的模型
pcl::SampleConsensusModelSphere<pcl::PointXYZ>::Ptr
model_s(new pcl::SampleConsensusModelSphere<pcl::PointXYZ>(cloud)); //球模型
pcl::SampleConsensusModelPlane<pcl::PointXYZ>::Ptr
model_p(new pcl::SampleConsensusModelPlane<pcl::PointXYZ>(cloud)); //平面模型
if (pcl::console::find_argument(argc argv "-f") >= 0)
{
pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_p); //随机采样一致性对象
ransac.setDistanceThreshold(.01); //设置距离阈值，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); //获取所得内点，结果保存在inliers中
}
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
//将模型中所有局内点复制到final中
pcl::copyPointCloud(*cloud inliers *final);
// creates the visualization object and adds either our original cloud or all of the inliers
// depending on the command line arguments specified.
//创建可视化对象
pcl::visualization::PCLVisualizer::Ptr 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);
std::this_thread::sleep_for(100ms);
}
return 0;
}
运行结果
无参数输入时
输入参数-f时
