PCL点云库SAC-IA+ICP点云配准拼接

采用SAC-IA(采样一致性初始配准算法)进行粗匹配得到大概位置，
再结合ICP（迭代最近点算法(Iterative Cloest Point, ICP)）算法进行精确配准。
绿色是源点云，红色是目标点云，蓝色是配准之后的点云）

#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 object
    pcl::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;
}

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:\\intern\\SAC-IA-master\\bun000_Structured.pcd", *cloud_src_o);
    PointCloud::Ptr cloud_tgt_o(new PointCloud);//目标点云
    pcl::io::loadPCDFile("E:\\intern\\SAC-IA-master\\bun045_Structured.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FPFH
    pcl::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("bunny_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("bunny_transformed_sac_ndt.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);
}

报错解决：
error C2079: “pcl::KdTreeFLANN::param_radius_”使用未定义的 struct“flann::SearchParams”

PCL和OpenCV库冲突，OpenCV的包含目录中opencv\build\include\opencv2\flann，解决方案是项目属性->VC++目录->包含目录中找到OpenCV的目录删除，或者在属性管理器中删除OpenCV的属性表，只留pcl的属性表。