简介:
对应点可视化代码详解
viewer->addCorrespondences<pcl::PointXYZ>(input_cloud, target_cloud, all_correspondences, "correspondence",v1);
pcl::PointXYZ表示所添加对应点对的类型为PointXYZ类型的,参数中的前两个表示源点云和目标点云,all_correspondences 存储从源点云到目标点云的对应点的索引,”correspondence“是自定义的标签,v1表示添加到哪个窗口。
为了使得对应点更加个性化,可以对它进行如下”定制“:
viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, 2, "correspondence");
设置对应点连线的粗细.
PCL_VISUALIZER_LINE_WIDTH,表示线操作,线段的宽度为2(线段的宽度最好不要超过自定义的点的大小), "correspondence"表示对 对应的标签 做处理。
viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0, 0, 0.5, "correspondence");
设置对应点连线的颜色,范围从0-1之间。
代码实现
#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <boost/thread/thread.hpp>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/registration/correspondence_estimation.h>
using namespace std;
int
main(int argc, char** argv)
{
// 加载第一次扫描点云数据作为目标云
pcl::PointCloud<pcl::PointXYZ>::Ptr target_cloud(new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile<pcl::PointXYZ>("A3 - Cloud.pcd", *target_cloud) == -1)
{
PCL_ERROR("读取目标点云失败 \n");
return (-1);
}
cout << "从目标点云中读取 " << target_cloud->size() << " 个点" << endl;
// 加载从新视角得到的第二次扫描点云数据作为源点云
pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud(new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile<pcl::PointXYZ>("A3 - Cloud_A.pcd", *input_cloud) == -1)
{
PCL_ERROR("读取源标点云失败 \n");
return (-1);
}
cout << "从源点云中读取 " << input_cloud->size() << " 个点" << endl;
//初始化对象
pcl::registration::CorrespondenceEstimation<pcl::PointXYZ, pcl::PointXYZ>core;
core.setInputSource(input_cloud);
core.setInputTarget(target_cloud);
pcl::Correspondences all_correspondences;
//core.determineCorrespondences(all_correspondences,6);//确定输入点云与目标点云之间的对应关系:
core.determineReciprocalCorrespondences(all_correspondences); //确定输入点云与目标点云之间的交互对应关系。
float sum = 0.0, rmse;
vector<float>Co;
for (size_t j = 0; j < all_correspondences.size(); j++){
sum += all_correspondences[j].distance;
Co.push_back(all_correspondences[j].distance);
}
rmse = sqrt(sum / all_correspondences.size());
vector<float>::iterator max = max_element(Co.begin(), Co.end());
vector<float>::iterator min = min_element(Co.begin(), Co.end());
cout << "匹配点对个数" << all_correspondences.size() << endl;
cout << "距离最大值" << sqrt(*max) * 100 << "厘米" << endl;
cout << "距离最小值" << sqrt(*min) * 100 << "厘米" << endl;
cout << "均方根误差" << rmse * 100 << "厘米" << endl;
boost::shared_ptr<pcl::visualization::PCLVisualizer>viewer(new pcl::visualization::PCLVisualizer("显示点云"));
viewer->setBackgroundColor(0, 0, 0); //设置背景颜色为黑色
// 对目标点云着色可视化 (red).
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>target_color(target_cloud, 255, 0, 0);
viewer->addPointCloud<pcl::PointXYZ>(target_cloud, target_color, "target cloud");
viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "target cloud");
// 对源点云着色可视化 (green).
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>input_color(input_cloud, 0, 255, 0);
viewer->addPointCloud<pcl::PointXYZ>(input_cloud, input_color, "input cloud");
viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "input cloud");
//对应关系可视化
viewer->addCorrespondences<pcl::PointXYZ>(input_cloud, target_cloud, all_correspondences, "correspondence");
//viewer->initCameraParameters();
while (!viewer->wasStopped())
{
viewer->spinOnce(100);
boost::this_thread::sleep(boost::posix_time::microseconds(100000));
}
system("pause");
return 0;
}
结果展示
1、输出对应关系
2、交互式查找结果:
3、必要说明
实验中发现对应关系中输出的distance,为对应点之间距离的平方