[PCL]计算关键点处的特征并且可视化(iss/sift+fpfh/pfh/shot)

2024-02-28 15:48
文章标签 计算 可视化 pcl 特征 sift shot 关键点 fpfh pfh iss

本文主要是介绍[PCL]计算关键点处的特征并且可视化(iss/sift+fpfh/pfh/shot)，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

目录

  • 计算关键点
  • 计算关键点处的特征描述
  • 建立匹配关系
  • 估计位姿(配准)
  • 可视化
    • 可视化关键点
    • 可视化特征直方图
    • 可视化对应关系
  • 参考
  • 全部代码

计算关键点

    clock_t start = clock();// 计算关键点pcl::ISSKeypoint3D<pcl::PointXYZ, pcl::PointXYZ> iss_det;pcl::search::KdTree<pcl::PointXYZ>::Ptr model_tree(new pcl::search::KdTree<pcl::PointXYZ>());pcl::search::KdTree<pcl::PointXYZ>::Ptr scene_tree(new pcl::search::KdTree<pcl::PointXYZ>());// 计算分辨率double model_resolution = computeCloudResolution(model);cout<<"model_resolution: "<<model_resolution<<endl;double scene_resolution = computeCloudResolution(scene);cout<<"model_resolution: "<<scene_resolution<<endl;//iss公共参数设置iss_det.setMinNeighbors(5);iss_det.setThreshold21(0.975);iss_det.setThreshold32(0.975);iss_det.setNumberOfThreads(4);// 计算model关键点iss_det.setInputCloud(model);iss_det.setSearchMethod(model_tree);iss_det.setSalientRadius(6*model_resolution);  // 0.5iss_det.setNonMaxRadius(4*model_resolution);iss_det.compute(*model_keypoint);// 计算scene关键点iss_det.setInputCloud(scene);iss_det.setSearchMethod(scene_tree);iss_det.setSalientRadius(6*scene_resolution);  // 0.5iss_det.setNonMaxRadius(4*scene_resolution);iss_det.compute(*scene_keypoint);clock_t end = clock();cout << "iss关键点提取时间：" << (double)(end - start) / CLOCKS_PER_SEC <<endl;cout << "model_iss关键点数量" << model_keypoint->size() << endl;cout << "scene_iss关键点数量" << scene_keypoint->size() << endl;

计算关键点处的特征描述

// 计算fpfh特征
// input: keypoints ,cloud , normals
// output: FPFH descriptors
void
compute_fpfh(pcl::PointCloud<pcl::PointXYZ>::Ptr keypoints, pcl::PointCloud<pcl::PointXYZ>::Ptr cloud,pcl::PointCloud<pcl::Normal>::Ptr normals,pcl::PointCloud<pcl::FPFHSignature33>::Ptr descriptors)
{clock_t start = clock();// FPFH estimation object.pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh;pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ> );fpfh.setInputCloud(keypoints);  // 计算keypoints处的特征fpfh.setInputNormals(normals);   // cloud的法线fpfh.setSearchSurface(cloud); // 计算的平面是cloud 而不是keypointsfpfh.setSearchMethod(kdtree);// Search radius, to look for neighbors. Note: the value given here has to be// larger than the radius used to estimate the normals.fpfh.setRadiusSearch(2.0);fpfh.compute(*descriptors);clock_t end = clock();cout<<"Time fpfh: "<< (double)(end - start) / CLOCKS_PER_SEC <<endl;cout<<"Get fpfh: "<<descriptors->points.size()<<endl;}

建立匹配关系

估计位姿(配准)

可视化

可视化关键点

//点云可视化
// 显示model+scene以及他们的keypoints
void
visualize_pcd(PointCloud::Ptr model, PointCloud::Ptr model_keypoints,PointCloud::Ptr scene, PointCloud::Ptr scene_keypoints)
{pcl::visualization::PCLVisualizer viewer("registration Viewer");pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> model_color(model, 0, 255, 0);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> scene_color(scene, 255, 0, 0);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> model_keypoint_color(model_keypoints, 0, 0, 255);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> scene_keypoint_color(scene_keypoints, 0, 0, 255);viewer.setBackgroundColor(255, 255, 255);viewer.addPointCloud(model, model_color, "model");viewer.addPointCloud(scene, scene_color, "scene");viewer.addPointCloud(model_keypoints, model_keypoint_color, "model_keypoints");viewer.addPointCloud(scene_keypoints, scene_keypoint_color, "scene_keypoints");viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "model_keypoints");viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "scene_keypoints");while(!viewer.wasStopped()){viewer.spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}
}

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可视化特征直方图

// 可视化直方图
void
visualize_Histogram( pcl::PointCloud<pcl::FPFHSignature33>::Ptr model_feature, pcl::PointCloud<pcl::FPFHSignature33>::Ptr scene_feature)
{pcl::visualization::PCLPlotter plotter;plotter.addFeatureHistogram(*model_feature, 33); //设置的很坐标长度，该值越大，则显示的越细致plotter.addFeatureHistogram(*scene_feature, 33); //设置的很坐标长度，该值越大，则显示的越细致plotter.plot();
}

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可视化对应关系

// 可视化对应关系viewer.addCorrespondences<pcl::PointXYZ>(model_keypoints,scene_keypoints,*model_scene_corrs);

参考

PCL/OpenNI tutorial 4: 3D object recognition (descriptors)
PCL/OpenNI tutorial 5: 3D object recognition (pipeline)
PCL学习：基于点云分类的目标识别

全部代码


/* 目标:对输入的model,scene计算.匹配关系,并估计位姿* 下采样->计算关键点(iss)->计算特征(fpfh)* 可视化点云以及关键点,并可视化其特征直方图*/
#include <pcl/registration/ia_ransac.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/features/normal_3d_omp.h>
#include <pcl/features/fpfh.h>
#include <pcl/features/pfh.h>
#include <pcl/features/shot_omp.h>
#include <pcl/search/kdtree.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/filter.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/registration/icp.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <time.h>
#include <iostream>
#include <pcl/keypoints/iss_3d.h>
#include <cstdlib>
#include <pcl/visualization/pcl_plotter.h>// 直方图的可视化 方法2
#include <pcl/registration/sample_consensus_prerejective.h>   // pose estimate
#include <pcl/keypoints/sift_keypoint.h>   // shift关键点相关
#include <pcl/pcl_macros.h>using pcl::NormalEstimation;
using pcl::search::KdTree;
typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloud;typedef pcl::PointXYZ PointType;
typedef pcl::Normal NormalType;typedef pcl::SHOT352 DescriptorType;
float descr_rad_ (3.0f);  // shot描述子的搜索半径// sift相关
namespace pcl
{template<>struct SIFTKeypointFieldSelector<PointXYZ>{inline floatoperator () (const PointXYZ &p) const{return p.z;}};
}double
computeCloudResolution(const pcl::PointCloud<pcl::PointXYZ>::ConstPtr& cloud)
{double resolution = 0.0;int numberOfPoints = 0;int nres;std::vector<int> indices(2);std::vector<float> squaredDistances(2);pcl::search::KdTree<pcl::PointXYZ> tree;tree.setInputCloud(cloud);for (size_t i = 0; i < cloud->size(); ++i){if (! pcl_isfinite((*cloud)[i].x))continue;// Considering the second neighbor since the first is the point itself.nres = tree.nearestKSearch(i, 2, indices, squaredDistances);if (nres == 2){resolution += sqrt(squaredDistances[1]);++numberOfPoints;}}if (numberOfPoints != 0)resolution /= numberOfPoints;return resolution;
}// 计算shift特征
void
compute_shift(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud,pcl::PointCloud<pcl::PointXYZ>::Ptr keypoints)
{clock_t start = clock();const float min_scale = 1;             //设置尺度空间中最小尺度的标准偏差const int n_octaves = 3;               //设置高斯金字塔组（octave）的数目const int n_scales_per_octave =1;     //设置每组（octave）计算的尺度const float min_contrast = 0.02;          //设置限制关键点检测的阈值pcl::SIFTKeypoint<pcl::PointXYZ, pcl::PointWithScale> sift;//创建sift关键点检测对象pcl::PointCloud<pcl::PointWithScale> result;sift.setInputCloud(cloud);//设置输入点云pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());sift.setSearchMethod(tree);//创建一个空的kd树对象tree，并把它传递给sift检测对象sift.setScales(min_scale, n_octaves, n_scales_per_octave);//指定搜索关键点的尺度范围sift.setMinimumContrast(min_contrast);//设置限制关键点检测的阈值sift.compute(result);//执行sift关键点检测，保存结果在resultcopyPointCloud(result, *keypoints);//将点类型pcl::PointWithScale的数据转换为点类型pcl::PointXYZ的数据clock_t end = clock();cout << "sift关键点提取时间：" << (double)(end - start) / CLOCKS_PER_SEC <<endl;cout << "sift关键点数量" << keypoints->size() << endl;
}// 计算iss特征
void
compute_iss(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud,pcl::PointCloud<pcl::PointXYZ>::Ptr keypoints)
{clock_t start = clock();// 计算关键点pcl::ISSKeypoint3D<pcl::PointXYZ, pcl::PointXYZ> iss_det;pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());// 计算分辨率double model_resolution = computeCloudResolution(cloud);cout<<"resolution: "<<model_resolution<<endl;//iss公共参数设置iss_det.setMinNeighbors(10);iss_det.setThreshold21(0.975);iss_det.setThreshold32(0.975);iss_det.setNumberOfThreads(4);// 计算model关键点iss_det.setInputCloud(cloud);iss_det.setSearchMethod(tree);iss_det.setSalientRadius(6*model_resolution);  // 0.5iss_det.setNonMaxRadius(4*model_resolution);iss_det.compute(*keypoints);clock_t end = clock();cout << "iss关键点提取时间：" << (double)(end - start) / CLOCKS_PER_SEC <<endl;cout << "iss关键点数量" << keypoints->size() << endl;}// 估计法线
// input:cloud
// output:normals
void
est_normals(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in,pcl::PointCloud<pcl::Normal>::Ptr normals)
{pcl::NormalEstimationOMP<PointType, NormalType> norm_est;norm_est.setNumberOfThreads(4);   //手动设置线程数norm_est.setKSearch (10);         //设置k邻域搜索阈值为10个点norm_est.setInputCloud (cloud_in);   //设置输入模型点云norm_est.compute (*normals);//计算点云法线}// 计算fpfh特征
// input: keypoints ,cloud , normals
// output: FPFH descriptors
void
compute_fpfh(pcl::PointCloud<pcl::PointXYZ>::Ptr keypoints, pcl::PointCloud<pcl::PointXYZ>::Ptr cloud,pcl::PointCloud<pcl::Normal>::Ptr normals,pcl::PointCloud<pcl::FPFHSignature33>::Ptr descriptors)
{clock_t start = clock();// FPFH estimation object.pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh;pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ> );fpfh.setInputCloud(keypoints);  // 计算keypoints处的特征fpfh.setInputNormals(normals);   // cloud的法线fpfh.setSearchSurface(cloud); // 计算的平面是cloud 而不是keypointsfpfh.setSearchMethod(kdtree);// Search radius, to look for neighbors. Note: the value given here has to be// larger than the radius used to estimate the normals.fpfh.setRadiusSearch(2.0);fpfh.compute(*descriptors);clock_t end = clock();cout<<"Time fpfh: "<< (double)(end - start) / CLOCKS_PER_SEC <<endl;cout<<"Get fpfh: "<<descriptors->points.size()<<endl;}// 计算pfh特征
// input: keypoints ,cloud , normals
// output: FPFH descriptors
void
compute_pfh(pcl::PointCloud<pcl::PointXYZ>::Ptr keypoints, pcl::PointCloud<pcl::PointXYZ>::Ptr cloud,pcl::PointCloud<pcl::Normal>::Ptr normals,pcl::PointCloud<pcl::PFHSignature125>::Ptr descriptors)
{clock_t start = clock();// FPFH estimation object.pcl::PFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::PFHSignature125> pfh;pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ> );pfh.setInputCloud(keypoints);  // 计算keypoints处的特征pfh.setInputNormals(normals);   // cloud的法线pfh.setSearchSurface(cloud); // 计算的平面是cloud 而不是keypointspfh.setSearchMethod(kdtree);// Search radius, to look for neighbors. Note: the value given here has to be// larger than the radius used to estimate the normals.pfh.setRadiusSearch(0.5);pfh.compute(*descriptors);clock_t end = clock();cout<<"Time pfh: "<< (double)(end - start) / CLOCKS_PER_SEC <<endl;cout<<"Get pfh: "<<descriptors->points.size()<<endl;}// 计算SHOT特征
// input:model_keypoint/scene_keypoint,  model/scene,  model_normals/scene_noemals
// outut:model_shot/scene_shot
void
compute_shot(pcl::PointCloud<pcl::PointXYZ>::Ptr keypoints, pcl::PointCloud<pcl::PointXYZ>::Ptr cloud,pcl::PointCloud<pcl::Normal>::Ptr normals,pcl::PointCloud<pcl::SHOT352>::Ptr descriptors)
{clock_t start=clock();pcl::SHOTEstimationOMP<PointType, NormalType, DescriptorType> descr_est;descr_est.setNumberOfThreads(4);   // 设置线程数 默认为0descr_est.setRadiusSearch (descr_rad_);     //设置搜索半径descr_est.setInputCloud (keypoints);  //模型点云的关键点descr_est.setInputNormals (normals);  //模型点云的法线descr_est.setSearchSurface (cloud);         //模型点云descr_est.compute(*descriptors);   // 计算,保存clock_t end=clock();cout<<"Time SHOT: "<< (double)(end - start) / CLOCKS_PER_SEC <<endl;cout<<"Get SHOT: "<<descriptors->points.size()<<endl;
}// fpfh match
// input: modelDescriptors,sceneDescriptors
// output: pcl::CorrespondencesPtr
void
find_match(pcl::PointCloud<pcl::FPFHSignature33>::Ptr model_descriptors,pcl::PointCloud<pcl::FPFHSignature33>::Ptr scene_descriptors,pcl::CorrespondencesPtr model_scene_corrs)
{clock_t start = clock();pcl::KdTreeFLANN<pcl::FPFHSignature33> matching;matching.setInputCloud(model_descriptors);for (size_t i = 0; i < scene_descriptors->size(); ++i){std::vector<int> neighbors(1);std::vector<float> squaredDistances(1);// Ignore NaNs.if (pcl_isfinite(scene_descriptors->at(i).histogram[0]   )){// Find the nearest neighbor (in descriptor space)...int neighborCount = matching.nearestKSearch(scene_descriptors->at(i), 1, neighbors, squaredDistances);// ...and add a new correspondence if the distance is less than a threshold// (SHOT distances are between 0 and 1, other descriptors use different metrics).if (neighborCount == 1 && squaredDistances[0] < 0.1f){pcl::Correspondence correspondence(neighbors[0], static_cast<int>(i), squaredDistances[0]);model_scene_corrs->push_back(correspondence);cout<<"( "<<correspondence.index_query<<","<<correspondence.index_match<<" )"<<endl;}}}std::cout << "Found " << model_scene_corrs->size() << " correspondences." << std::endl;clock_t end = clock();cout<<"Time match: "<< (double)(end - start) / CLOCKS_PER_SEC <<endl;cout<<"-----------------------------"<<endl;
}// shot match
// input: modelDescriptors,sceneDescriptors
// output: pcl::CorrespondencesPtr
void
find_match_shot(pcl::PointCloud<pcl::SHOT352>::Ptr model_descriptors,pcl::PointCloud<pcl::SHOT352>::Ptr scene_descriptors,pcl::CorrespondencesPtr model_scene_corrs)
{clock_t start = clock();pcl::KdTreeFLANN<pcl::SHOT352> matching;matching.setInputCloud(model_descriptors);for (size_t i = 0; i < scene_descriptors->size(); ++i){std::vector<int> neighbors(1);std::vector<float> squaredDistances(1);// Ignore NaNs.if (pcl_isfinite(scene_descriptors->at(i).descriptor[0]   )){// Find the nearest neighbor (in descriptor space)...int neighborCount = matching.nearestKSearch(scene_descriptors->at(i), 1, neighbors, squaredDistances);// ...and add a new correspondence if the distance is less than a threshold// (SHOT distances are between 0 and 1, other descriptors use different metrics).if (neighborCount == 1 && squaredDistances[0] < 0.05f){pcl::Correspondence correspondence(neighbors[0], static_cast<int>(i), squaredDistances[0]);model_scene_corrs->push_back(correspondence);
//                cout<<"( "<<correspondence.index_query<<","<<correspondence.index_match<<" )"<<endl;   // 打印对应点对}}}std::cout << "Found " << model_scene_corrs->size() << " correspondences." << std::endl;clock_t end = clock();cout<<"Time match: "<< (double)(end - start) / CLOCKS_PER_SEC <<endl;cout<<"-----------------------------"<<endl;
}// pfh match
// input: modelDescriptors,sceneDescriptors
// output: pcl::CorrespondencesPtr
void
find_match_pfh(pcl::PointCloud<pcl::PFHSignature125>::Ptr model_descriptors,pcl::PointCloud<pcl::PFHSignature125>::Ptr scene_descriptors,pcl::CorrespondencesPtr model_scene_corrs)
{clock_t start = clock();pcl::KdTreeFLANN<pcl::PFHSignature125> matching;matching.setInputCloud(model_descriptors);for (size_t i = 0; i < scene_descriptors->size(); ++i){std::vector<int> neighbors(1);std::vector<float> squaredDistances(1);// Ignore NaNs.if (pcl_isfinite(scene_descriptors->at(i).histogram[0]   )){// Find the nearest neighbor (in descriptor space)...int neighborCount = matching.nearestKSearch(scene_descriptors->at(i), 1, neighbors, squaredDistances);// ...and add a new correspondence if the distance is less than a threshold// (SHOT distances are between 0 and 1, other descriptors use different metrics).if (neighborCount == 1 && squaredDistances[0] < 0.1f){pcl::Correspondence correspondence(neighbors[0], static_cast<int>(i), squaredDistances[0]);model_scene_corrs->push_back(correspondence);
//                cout<<"( "<<correspondence.index_query<<","<<correspondence.index_match<<" )"<<endl;   // 打印对应点对}}}std::cout << "Found " << model_scene_corrs->size() << " correspondences." << std::endl;clock_t end = clock();cout<<"Time match: "<< (double)(end - start) / CLOCKS_PER_SEC <<endl;cout<<"-----------------------------"<<endl;
}// 位姿估计
// input: model,scene,model_descriptors,scene_descriptors
// output: R,t
void
estimationPose(pcl::PointCloud<pcl::PointXYZ>::Ptr model,pcl::PointCloud<pcl::PointXYZ>::Ptr scene,pcl::PointCloud<pcl::PFHSignature125>::Ptr model_descriptors,pcl::PointCloud<pcl::PFHSignature125>::Ptr scene_descriptors,pcl::PointCloud<pcl::PointXYZ>::Ptr alignedModel)
{// Object for pose estimation.pcl::SampleConsensusPrerejective<pcl::PointXYZ, pcl::PointXYZ, pcl::PFHSignature125> pose;pose.setInputSource(model);pose.setInputTarget(scene);pose.setSourceFeatures(model_descriptors);pose.setTargetFeatures(scene_descriptors);// Instead of matching a descriptor with its nearest neighbor, choose randomly between// the N closest ones, making it more robust to outliers, but increasing time.pose.setCorrespondenceRandomness(5);   // 匹配最近的5个描述子// Set the fraction (0-1) of inlier points required for accepting a transformation.// At least this number of points will need to be aligned to accept a pose.pose.setInlierFraction(0.01f);    //内点的数量// Set the number of samples to use during each iteration (minimum for 6 DoF is 3).pose.setNumberOfSamples(3);     // 估计6DOF需要3个点// Set the similarity threshold (0-1 between edge lengths of the polygons. The// closer to 1, the more strict the rejector will be, probably discarding acceptable poses.pose.setSimilarityThreshold(0.2f);// Set the maximum distance threshold between two correspondent points in source and target.// If the distance is larger, the points will be ignored in the alignment process.pose.setMaxCorrespondenceDistance(1.0f);  // 允许的最大距离pose.setMaximumIterations(50000);   // 迭代次数pose.align(*alignedModel);if (pose.hasConverged()){Eigen::Matrix4f transformation = pose.getFinalTransformation();Eigen::Matrix3f rotation = transformation.block<3, 3>(0, 0);Eigen::Vector3f translation = transformation.block<3, 1>(0, 3);std::cout << "Transformation matrix:" << std::endl << std::endl;printf("\t\t    | %6.3f %6.3f %6.3f | \n", rotation(0, 0), rotation(0, 1), rotation(0, 2));printf("\t\tR = | %6.3f %6.3f %6.3f | \n", rotation(1, 0), rotation(1, 1), rotation(1, 2));printf("\t\t    | %6.3f %6.3f %6.3f | \n", rotation(2, 0), rotation(2, 1), rotation(2, 2));std::cout << std::endl;printf("\t\tt = < %0.3f, %0.3f, %0.3f >\n", translation(0), translation(1), translation(2));}else std::cout << "Did not converge." << std::endl;
}//点云可视化
// 显示model+scene以及他们的keypoints
void
visualize_pcd(PointCloud::Ptr model, PointCloud::Ptr model_keypoints,PointCloud::Ptr scene, PointCloud::Ptr scene_keypoints)
{pcl::visualization::PCLVisualizer viewer("registration Viewer");pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> model_color(model, 0, 255, 0);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> scene_color(scene, 255, 0, 0);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> model_keypoint_color(model_keypoints, 0, 0, 255);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> scene_keypoint_color(scene_keypoints, 0, 0, 255);viewer.setBackgroundColor(255, 255, 255);viewer.addPointCloud(model, model_color, "model");viewer.addPointCloud(scene, scene_color, "scene");viewer.addPointCloud(model_keypoints, model_keypoint_color, "model_keypoints");viewer.addPointCloud(scene_keypoints, scene_keypoint_color, "scene_keypoints");viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "model_keypoints");viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "scene_keypoints");while(!viewer.wasStopped()){viewer.spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}
}// 可视化对应关系
//input: model_keypoints, scene_keypoints, model_scene_corrs
void
visualize_corrs(pcl::PointCloud<pcl::PointXYZ>::Ptr model_keypoints,pcl::PointCloud<pcl::PointXYZ>::Ptr scene_keypoints,pcl::PointCloud<pcl::PointXYZ>::Ptr model,pcl::PointCloud<pcl::PointXYZ>::Ptr scene,pcl::CorrespondencesPtr model_scene_corrs)
{// 添加关键点pcl::visualization::PCLVisualizer viewer("corrs Viewer");viewer.setBackgroundColor(255, 255, 255);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> model_color(model, 0, 255, 0);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> scene_color(scene, 255, 0, 0);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> model_keypoint_color(model_keypoints, 0, 255, 255);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> scene_keypoint_color(scene_keypoints, 0, 0, 255);
//    viewer.addPointCloud(model, model_color, "model");
//    viewer.addPointCloud(scene, scene_color, "scene");viewer.addPointCloud(model_keypoints, model_keypoint_color, "model_keypoints");viewer.addPointCloud(scene_keypoints, scene_keypoint_color, "scene_keypoints");viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "model_keypoints");viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "scene_keypoints");// 可视化对应关系viewer.addCorrespondences<pcl::PointXYZ>(model_keypoints,scene_keypoints,*model_scene_corrs);
//    //添加对应关系
//    int i=1;
//    for(auto iter=model_scene_corrs->begin();iter!=model_scene_corrs->end();++iter)
//    {
//        std::stringstream ss_line;
//        ss_line << "correspondence_line" << i ;
//        i++;
//        PointType& model_point = model_keypoints->at (iter->index_query);  // 从corrs中获取对应点
//        PointType& scene_point = scene_keypoints->at (iter->index_match);
//        viewer.addLine<PointType, PointType> (model_point, scene_point, 0, 0, 0, ss_line.str ());
//        viewer.setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, 5, ss_line.str ());   // 设置线宽
//
//
//
//    }// 显示while(!viewer.wasStopped()){viewer.spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}}// 可视化直方图
void
visualize_Histogram( pcl::PointCloud<pcl::FPFHSignature33>::Ptr model_feature, pcl::PointCloud<pcl::FPFHSignature33>::Ptr scene_feature)
{pcl::visualization::PCLPlotter plotter;plotter.addFeatureHistogram(*model_feature, 33); //设置的很坐标长度，该值越大，则显示的越细致plotter.addFeatureHistogram(*scene_feature, 33); //设置的很坐标长度，该值越大，则显示的越细致plotter.plot();
}int main(int argc, char** argv)
{PointCloud::Ptr aligned_model(new PointCloud);    // 变换之后的点云pcl::CorrespondencesPtr model_scene_corrs (new pcl::Correspondences ());  // model-scene的匹配关系pcl::PointCloud<pcl::SHOT352>::Ptr model_descriptors_shot(new pcl::PointCloud<pcl::SHOT352>());  // shot特征pcl::PointCloud<pcl::SHOT352>::Ptr scene_descriptors_shot(new pcl::PointCloud<pcl::SHOT352>());pcl::PointCloud<pcl::FPFHSignature33>::Ptr model_descriptors(new pcl::PointCloud<pcl::FPFHSignature33>());  // fpfh特征pcl::PointCloud<pcl::FPFHSignature33>::Ptr scene_descriptors(new pcl::PointCloud<pcl::FPFHSignature33>());pcl::PointCloud<pcl::PFHSignature125>::Ptr model_descriptors_pfh(new pcl::PointCloud<pcl::PFHSignature125>());  // pfh特征pcl::PointCloud<pcl::PFHSignature125>::Ptr scene_descriptors_pfh(new pcl::PointCloud<pcl::PFHSignature125>());PointCloud::Ptr model_keypoint(new PointCloud);                 // iss关键点PointCloud::Ptr scene_keypoint(new PointCloud);pcl::PointCloud<pcl::PointXYZ>::Ptr model_keypoints_shift(new pcl::PointCloud<pcl::PointXYZ>);  // shift关键点pcl::PointCloud<pcl::PointXYZ>::Ptr scene_keypoints_shift(new pcl::PointCloud<pcl::PointXYZ>);PointCloud::Ptr cloud_src_model(new PointCloud);    //model点云  读入PointCloud::Ptr cloud_src_scene(new PointCloud);    //scene点云PointCloud::Ptr model(new PointCloud);    //model点云  降采样后,一切计算是以这个为基础的PointCloud::Ptr scene(new PointCloud);    //scene点云pcl::PointCloud<pcl::Normal>::Ptr model_normals(new pcl::PointCloud<pcl::Normal>);  // 法向量pcl::PointCloud<pcl::Normal>::Ptr scene_normals(new pcl::PointCloud<pcl::Normal>);//加载点云if (argc<3){cout<<".exe model.pcd scene.pcd"<<endl;return -1;}pcl::io::loadPCDFile(argv[1], *cloud_src_model);pcl::io::loadPCDFile(argv[2], *cloud_src_scene);cout << "/" << endl;cout << "原始model点云数量：" << cloud_src_model->size() << endl;cout << "原始scene点云数量：" << cloud_src_scene->size() << endl;model=cloud_src_model;scene=cloud_src_scene;//    //均匀采样对象
//    pcl::VoxelGrid<pcl::PointXYZ> filter;
//    filter.setLeafSize(2,2,2);
//    filter.setInputCloud(cloud_src_model);
//    filter.filter(*model);
//    cout << "降采样后点云数量：" << model->size() << endl;
//
//    filter.setInputCloud(cloud_src_scene);
//    filter.filter(*scene);
//    cout << "降采样后点云数量：" << scene->size() << endl;// 计算降采样后的点云的法线est_normals(model,model_normals);est_normals(scene,scene_normals);// 计算关键点
//    compute_iss(model,model_keypoint);
//    compute_iss(scene,scene_keypoint);compute_shift(model,model_keypoints_shift);compute_shift(scene,scene_keypoints_shift);// 根据关键点计算特征
//    compute_fpfh(model_keypoint,model,model_normals,model_descriptors);   // fpfh特征
//    compute_fpfh(scene_keypoint,scene,scene_normals,scene_descriptors);
//    compute_shot(model_keypoint,model,model_normals,model_descriptors_shot);   // shot特征
//    compute_shot(scene_keypoint,scene,scene_normals,scene_descriptors_shot);compute_pfh(model_keypoints_shift,model,model_normals,model_descriptors_pfh);   // pfhcompute_pfh(scene_keypoints_shift,scene,scene_normals,scene_descriptors_pfh);//match  得到对应关系:model_scene_corrs
//    find_match(model_descriptors,scene_descriptors,model_scene_corrs);        // fpfh
//    find_match_shot(model_descriptors_shot,scene_descriptors_shot,model_scene_corrs);           // shotfind_match_pfh(model_descriptors_pfh,scene_descriptors_pfh,model_scene_corrs);      // pfh// pose estimte
//    estimationPose(model_keypoint,scene_keypoint,model_descriptors_pfh,scene_descriptors_pfh,aligned_model);//可视化
//    visualize_pcd(model,model_keypoint,scene, scene_keypoint);  // 降采样后的点云 , 特征点visualize_corrs(model_keypoints_shift,scene_keypoints_shift,model,scene,model_scene_corrs);  // 可视化对应关系
//    visualize_Histogram(model_descriptors,scene_descriptors);   // 可视化fpfh直方图return 0;
}

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