基于PCL的三维重建——点云配准（二）SAC-IA+ICP算法的实践

此次尝试了用SAC-IA的粗配准加上ICP算法的精细配准，实验的数据是我们自已用深度相机采集的。

过程中进行了去除NAN点，下采用滤波，计算表面法线，计算FPFH，SAC-IA配准，ICP配准。

#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/voxel_grid.h>#include <pcl/filters/filter.h>#include <pcl/registration/icp.h>#include <pcl/visualization/pcl_visualizer.h>#include <time.h>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){//int vp_1, vp_2;// Create a PCLVisualizer objectpcl::visualization::PCLVisualizer viewer("registration Viewer");//viewer.createViewPort (0.0, 0, 0.5, 1.0, vp_1);// viewer.createViewPort (0.5, 0, 1.0, 1.0, vp_2);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.addPointCloud(pcd_src, src_h, "source cloud");viewer.addPointCloud(pcd_tgt, tgt_h, "tgt cloud");viewer.addPointCloud(pcd_final, final_h, "final cloud");//viewer.addCoordinateSystem(1.0);while (!viewer.wasStopped()){viewer.spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}}//由旋转平移矩阵计算旋转角度void matrix2angle(Eigen::Matrix4f &result_trans, Eigen::Vector3f &result_angle){double ax, ay, az;if (result_trans(2, 0) == 1 || result_trans(2, 0) == -1){az = 0;double dlta;dlta = atan2(result_trans(0, 1), result_trans(0, 2));if (result_trans(2, 0) == -1){ay = M_PI / 2;ax = az + dlta;}else{ay = -M_PI / 2;ax = -az + dlta;}}else{ay = -asin(result_trans(2, 0));ax = atan2(result_trans(2, 1) / cos(ay), result_trans(2, 2) / cos(ay));az = atan2(result_trans(1, 0) / cos(ay), result_trans(0, 0) / cos(ay));}result_angle << ax, ay, az;}intmain(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("haihang_nose.pcd", *cloud_src_o);PointCloud::Ptr cloud_tgt_o(new PointCloud);//目标点云pcl::io::loadPCDFile("linlin_nose.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" << endl;//下采样滤波pcl::VoxelGrid<pcl::PointXYZ> voxel_grid;voxel_grid.setLeafSize(0.012, 0.012, 0.012);voxel_grid.setInputCloud(cloud_src_o);PointCloud::Ptr cloud_src(new PointCloud);voxel_grid.filter(*cloud_src);std::cout << "down size *cloud_src_o from " << cloud_src_o->size() << "to" << cloud_src->size() << endl;pcl::io::savePCDFileASCII("bunny_src_down.pcd", *cloud_src);//计算表面法线pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne_src;ne_src.setInputCloud(cloud_src);pcl::search::KdTree< pcl::PointXYZ>::Ptr tree_src(new pcl::search::KdTree< pcl::PointXYZ>());ne_src.setSearchMethod(tree_src);pcl::PointCloud<pcl::Normal>::Ptr cloud_src_normals(new pcl::PointCloud< pcl::Normal>);ne_src.setRadiusSearch(0.02);ne_src.compute(*cloud_src_normals);std::vector<int> indices_tgt;pcl::removeNaNFromPointCloud(*cloud_tgt_o, *cloud_tgt_o, indices_tgt);std::cout << "remove *cloud_tgt_o nan" << endl;pcl::VoxelGrid<pcl::PointXYZ> voxel_grid_2;voxel_grid_2.setLeafSize(0.01, 0.01, 0.01);voxel_grid_2.setInputCloud(cloud_tgt_o);PointCloud::Ptr cloud_tgt(new PointCloud);voxel_grid_2.filter(*cloud_tgt);std::cout << "down size *cloud_tgt_o.pcd from " << cloud_tgt_o->size() << "to" << cloud_tgt->size() << endl;pcl::io::savePCDFileASCII("bunny_tgt_down.pcd", *cloud_tgt);pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne_tgt;ne_tgt.setInputCloud(cloud_tgt);pcl::search::KdTree< pcl::PointXYZ>::Ptr tree_tgt(new pcl::search::KdTree< pcl::PointXYZ>());ne_tgt.setSearchMethod(tree_tgt);pcl::PointCloud<pcl::Normal>::Ptr cloud_tgt_normals(new pcl::PointCloud< pcl::Normal>);//ne_tgt.setKSearch(20);ne_tgt.setRadiusSearch(0.02);ne_tgt.compute(*cloud_tgt_normals);//计算FPFHpcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh_src;fpfh_src.setInputCloud(cloud_src);fpfh_src.setInputNormals(cloud_src_normals);pcl::search::KdTree<PointT>::Ptr tree_src_fpfh(new pcl::search::KdTree<PointT>);fpfh_src.setSearchMethod(tree_src_fpfh);pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs_src(new pcl::PointCloud<pcl::FPFHSignature33>());fpfh_src.setRadiusSearch(0.05);fpfh_src.compute(*fpfhs_src);std::cout << "compute *cloud_src fpfh" << endl;pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh_tgt;fpfh_tgt.setInputCloud(cloud_tgt);fpfh_tgt.setInputNormals(cloud_tgt_normals);pcl::search::KdTree<PointT>::Ptr tree_tgt_fpfh(new pcl::search::KdTree<PointT>);fpfh_tgt.setSearchMethod(tree_tgt_fpfh);pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs_tgt(new pcl::PointCloud<pcl::FPFHSignature33>());fpfh_tgt.setRadiusSearch(0.05);fpfh_tgt.compute(*fpfhs_tgt);std::cout << "compute *cloud_tgt fpfh" << endl;//SAC配准pcl::SampleConsensusInitialAlignment<pcl::PointXYZ, pcl::PointXYZ, pcl::FPFHSignature33> scia;scia.setInputSource(cloud_src);scia.setInputTarget(cloud_tgt);scia.setSourceFeatures(fpfhs_src);scia.setTargetFeatures(fpfhs_tgt);//scia.setMinSampleDistance(1);//scia.setNumberOfSamples(2);//scia.setCorrespondenceRandomness(20);PointCloud::Ptr sac_result(new PointCloud);scia.align(*sac_result);std::cout << "sac has converged:" << scia.hasConverged() << "  score: " << scia.getFitnessScore() << endl;Eigen::Matrix4f sac_trans;sac_trans = scia.getFinalTransformation();std::cout << sac_trans << endl;pcl::io::savePCDFileASCII("junjie_transformed_sac.pcd", *sac_result);clock_t sac_time = clock();//icp配准PointCloud::Ptr icp_result(new PointCloud);pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;icp.setInputSource(cloud_src);icp.setInputTarget(cloud_tgt_o);//Set the max correspondence distance to 4cm (e.g., correspondences with higher distances will be ignored)icp.setMaxCorrespondenceDistance(0.04);// 最大迭代次数icp.setMaximumIterations(50);// 两次变化矩阵之间的差值icp.setTransformationEpsilon(1e-10);// 均方误差icp.setEuclideanFitnessEpsilon(0.2);icp.align(*icp_result, sac_trans);clock_t end = clock();cout << "total time: " << (double)(end - start) / (double)CLOCKS_PER_SEC << " s" << endl;//我把计算法线和点特征直方图的时间也算在SAC里面了cout << "sac time: " << (double)(sac_time - start) / (double)CLOCKS_PER_SEC << " s" << endl;cout << "icp time: " << (double)(end - sac_time) / (double)CLOCKS_PER_SEC << " s" << endl;std::cout << "ICP has converged:" << icp.hasConverged()<< " score: " << icp.getFitnessScore() << std::endl;Eigen::Matrix4f icp_trans;icp_trans = icp.getFinalTransformation();//cout<<"ransformationProbability"<<icp.getTransformationProbability()<<endl;std::cout << icp_trans << endl;//使用创建的变换对未过滤的输入点云进行变换pcl::transformPointCloud(*cloud_src_o, *icp_result, icp_trans);//保存转换的输入点云pcl::io::savePCDFileASCII("junjie_transformed_sac_icp.pcd", *icp_result);//计算误差Eigen::Vector3f ANGLE_origin;ANGLE_origin << 0, 0, M_PI / 5;double error_x, error_y, error_z;Eigen::Vector3f ANGLE_result;matrix2angle(icp_trans, ANGLE_result);error_x = fabs(ANGLE_result(0)) - fabs(ANGLE_origin(0));error_y = fabs(ANGLE_result(1)) - fabs(ANGLE_origin(1));error_z = fabs(ANGLE_result(2)) - fabs(ANGLE_origin(2));cout << "original angle in x y z:\n" << ANGLE_origin << endl;cout << "error in aixs_x: " << error_x << "  error in aixs_y: " << error_y << "  error in aixs_z: " << error_z << endl;//可视化visualize_pcd(cloud_src_o, cloud_tgt_o, icp_result);return (0);}