PCL：在平面模型上构造凸凹多边形

#include <pcl/ModelCoefficients.h> #include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/sample_consensus/method_types.h> #include <pcl/sample_consensus/model_types.h> #include <pcl/filters/passthrough.h> #include <pcl/filters/project_inliers.h> #include <pcl/segmentation/sac_segmentation.h> #include <pcl/surface/concave_hull.h> #include <pcl/visualization/pcl_visualizer.h> #pragma comment(lib,"User32.lib") #pragma comment(lib, "gdi32.lib") int main(int argc, char** argv) { pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>), cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>), cloud_projected(new pcl::PointCloud<pcl::PointXYZ>); pcl::PCDReader reader; reader.read("table_scene_mug_stereo_textured.pcd", *cloud); //建立直通滤波器，消除杂散的NaN点 pcl::PassThrough<pcl::PointXYZ> pass; pass.setInputCloud(cloud); //设置输入点云 pass.setFilterFieldName("z");//设置分隔字段为z坐标 pass.setFilterLimits(0, 1.1);//设置分割阈值范围，z轴上不在该范围的点过滤掉 pass.filter(*cloud_filtered); std::cerr << "PointCloud after filtering has: " << cloud_filtered->points.size() << " data points." << std::endl; pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients); pcl::PointIndices::Ptr inliers(new pcl::PointIndices);//inliers指针存储点云分割后的结果 // Create the segmentation object pcl::SACSegmentation<pcl::PointXYZ> seg;//创建分割对象 // 可选设置 seg.setOptimizeCoefficients(true); //设置优化系数，该参数为可选设置 // 必须设置 seg.setModelType(pcl::SACMODEL_PLANE); //设置分割模型为SACMODEL_PLANE 平面模型 seg.setMethodType(pcl::SAC_RANSAC); //设置采样一致性估计方法模型为SAC_RANSAC seg.setDistanceThreshold(0.01); //设置距离阈值为0.01， 即与估计平面模型的距离小于0.01m的点都为内点inliers seg.setInputCloud(cloud_filtered); //设置输入点云为滤波后的点云 seg.segment(*inliers, *coefficients); //分割结果：平面模型 std::cerr << "PointCloud after segmentation has: " << inliers->indices.size() << " inliers." << std::endl; // Project the model inliers pcl::ProjectInliers<pcl::PointXYZ> proj; //创建点云投影滤波对象 proj.setModelType(pcl::SACMODEL_PLANE); //设置投影模型为SACMODEL_PLANE proj.setInputCloud(cloud_filtered); //设置输入点云为滤波后的点云 proj.setModelCoefficients(coefficients);//将估计得到的平面模型coefficients参数设置为投影平面模型系数 proj.filter(*cloud_projected); //将滤波后的点云投影到平面模型中得到投影后的点云cloud_projected std::cerr << "PointCloud after projection has: " << cloud_projected->points.size() << " data points." << std::endl; // Create a Concave Hull representation of the projected inliers pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_hull(new pcl::PointCloud<pcl::PointXYZ>); pcl::ConcaveHull<pcl::PointXYZ> chull;//创建多边形提取对象 chull.setInputCloud(cloud_projected);//设置输入点云为投影后的点云cloud_projected chull.setAlpha(0.1); //设置Alpha值为0.1 chull.reconstruct(*cloud_hull); //重建提取创建凹多边形 std::cerr << "Concave hull has: " << cloud_hull->points.size() << " data points." << std::endl; pcl::PCDWriter writer; writer.write("table_scene_mug_stereo_textured_hull.pcd", *cloud_hull, false); pcl::visualization::PCLVisualizer::Ptr viewer0(new pcl::visualization::PCLVisualizer("hull")); viewer0->addPointCloud(cloud, pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>(cloud, 0, 0, 255), "cloud"); viewer0->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 4, "cloud"); cout << "click q key to quit the visualizer and continue！！" << endl; //viewer0->spin(); pcl::visualization::PCLVisualizer::Ptr viewer1(new pcl::visualization::PCLVisualizer("hull")); viewer1->addPointCloud(cloud_hull, pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>(cloud_hull, 0, 255, 0), "cloud_hull"); viewer1->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 4, "cloud_hull"); cout << "click q key to quit the visualizer and continue！！" << endl; viewer1->spin(); return (0); }