#include <pcl/registration/ia_ransac.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/fpfh.h>
#include <pcl/search/kdtree.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/filter.h>
#include <pcl/registration/icp.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <time.h>
#include <pcl/common/io.h>
#include <iostream>
#include <pcl/keypoints/sift_keypoint.h>//关键点检测using pcl::NormalEstimation;
using pcl::search::KdTree;
typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloud;//点云可视化
void visualize_pcd(PointCloud::Ptr pcd_src,PointCloud::Ptr pcd_tgt)//PointCloud::Ptr pcd_final)
{pcl::visualization::PCLVisualizer viewer("registration Viewer");pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> src_h(pcd_src, 0, 255, 0);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> tgt_h(pcd_tgt, 255, 0, 0);//pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> final_h(pcd_final, 0, 0, 255);viewer.setBackgroundColor(255, 255, 255);viewer.addPointCloud(pcd_src, src_h, "source cloud");viewer.addPointCloud(pcd_tgt, tgt_h, "tgt cloud");//viewer.addPointCloud(pcd_final, final_h, "final cloud");viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "tgt cloud");while (!viewer.wasStopped()){viewer.spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}//pcl中sift特征需要返回强度信息，改为如下:
}
namespace pcl
{template<>struct SIFTKeypointFieldSelector<PointXYZ>{inline floatoperator () (const PointXYZ &p) const{return p.z;}};
}int
main(int argc, char** argv)
{//加载点云文件PointCloud::Ptr cloud_src_o(new PointCloud);//原点云，待配准pcl::io::loadPCDFile("E:/PointCloud/data/dragon/dragon.pcd", *cloud_src_o);cout << "原始点云数量："<<cloud_src_o->size() << endl;//PointCloud::Ptr cloud_tgt_o(new PointCloud);//目标点云//pcl::io::loadPCDFile("E:/PointCloud/data/pc_4.pcd", *cloud_tgt_o);//clock_t start = clock();//去除NAN点//std::vector<int> indices_src; //保存去除的点的索引//pcl::removeNaNFromPointCloud(*cloud_src_o, *cloud_src_o, indices_src);//std::cout << "remove *cloud_src_o nan" << cloud_src_o->size()<<endl;//std::vector<int> indices_tgt;//pcl::removeNaNFromPointCloud(*cloud_tgt_o, *cloud_tgt_o, indices_tgt);//std::cout << "remove *cloud_tgt_o nan" << cloud_tgt_o->size()<<endl;//设定参数值const float min_scale = 0.002f; //the standard deviation of the smallest scale in the scale spaceconst int n_octaves = 3;//尺度空间层数,小、关键点多const int n_scales_per_octave = 3;//the number of scales to compute within each octaveconst float min_contrast = 0.0001f;//根据点云，设置大小，越小关键点越多//sift关键点检测pcl::SIFTKeypoint<pcl::PointXYZ, pcl::PointWithScale > sift_src;pcl::PointCloud<pcl::PointWithScale> result_src;pcl::search::KdTree<pcl::PointXYZ>::Ptr tree_src(new pcl::search::KdTree<pcl::PointXYZ>());sift_src.setSearchMethod(tree_src);sift_src.setScales(min_scale, n_octaves, n_scales_per_octave);sift_src.setMinimumContrast(min_contrast);sift_src.setInputCloud(cloud_src_o);sift_src.compute(result_src);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" << result_src.size() << endl;PointCloud::Ptr cloud_src(new PointCloud);pcl::copyPointCloud(result_src, *cloud_src);//可视化visualize_pcd(cloud_src_o, cloud_src);return (0);
}