PCL点云的特征点提取同名点匹配并进行配准并附上代码

以下是基于PCL库实现PCL点云特征点提取、同名点匹配和配准的代码示例：

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/icp.h>
#include <pcl/registration/ndt.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/fpfh.h>
#include <pcl/visualization/pcl_visualizer.h>

int main(int argc, char** argv)
{
    // Load point clouds
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_source(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_target(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::io::loadPCDFile<pcl::PointXYZ>("source.pcd", *cloud_source);
    pcl::io::loadPCDFile<pcl::PointXYZ>("target.pcd", *cloud_target);

    // Compute surface normals
    pcl::PointCloud<pcl::Normal>::Ptr normals_source(new pcl::PointCloud<pcl::Normal>);
    pcl::PointCloud<pcl::Normal>::Ptr normals_target(new pcl::PointCloud<pcl::Normal>);
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
    pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
    ne.setSearchMethod(tree);
    ne.setInputCloud(cloud_source);
    ne.setKSearch(20);
    ne.compute(*normals_source);
    ne.setInputCloud(cloud_target);
    ne.compute(*normals_target);

    // Compute FPFH features
    pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs_source(new pcl::PointCloud<pcl::FPFHSignature33>);
    pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs_target(new pcl::PointCloud<pcl::FPFHSignature33>);
    pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh;
    fpfh.setSearchMethod(tree);
    fpfh.setInputCloud(cloud_source);
    fpfh.setInputNormals(normals_source);
    fpfh.setRadiusSearch(0.5);
    fpfh.compute(*fpfhs_source);
    fpfh.setInputCloud(cloud_target);
    fpfh.setInputNormals(normals_target);
    fpfh.compute(*fpfhs_target);

    // Find correspondences
    pcl::CorrespondencesPtr correspondences(new pcl::Correspondences);
    pcl::registration::CorrespondenceEstimation<pcl::FPFHSignature33, pcl::FPFHSignature33> est;
    est.setInputSource(fpfhs_source);
    est.setInputTarget(fpfhs_target);
    est.determineCorrespondences(*correspondences);

    // Reject correspondences based on distance and angle
    pcl::CorrespondencesPtr correspondences_filtered(new pcl::Correspondences);
    for (int i = 0; i < correspondences->size(); i++)
    {
        float dist = pcl::geometry::distance(cloud_source->at(correspondences->at(i).index_query),
                                              cloud_target->at(correspondences->at(i).index_match));
        float angle = pcl::geometry::acos3(cloud_source->at(correspondences->at(i).index_query).getNormalVector3fMap(),
                                           cloud_target->at(correspondences->at(i).index_match).getNormalVector3fMap());
        if (dist < 0.5 && angle < pcl::deg2rad(30))
        {
            correspondences_filtered->push_back(correspondences->at(i));
        }
    }

    // Visualize correspondences
    pcl::visualization::PCLVisualizer viewer("Correspondences");
    viewer.addPointCloud(cloud_source, "cloud_source");
    viewer.addPointCloud(cloud_target, "cloud_target");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1, 0, 0, "cloud_source");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0, 1, 0, "cloud_target");
    for (int i = 0; i < correspondences_filtered->size(); i++)
    {
        pcl::PointXYZ& p1 = cloud_source->at(correspondences_filtered->at(i).index_query);
        pcl::PointXYZ& p2 = cloud_target->at(correspondences_filtered->at(i).index_match);
        std::stringstream ss;
        ss << "correspondence_" << i;
        viewer.addLine(p1, p2, 1, 1, 1, ss.str());
    }
    viewer.spin();

    // Perform ICP registration
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_source_registered(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
    icp.setInputSource(cloud_source);
    icp.setInputTarget(cloud_target);
    icp.setCorrespondenceEstimation(new pcl::registration::CorrespondenceEstimationBackProjection<pcl::PointXYZ, pcl::PointXYZ>);
    icp.setCorrespondences(correspondences_filtered);
    icp.setMaximumIterations(100);
    icp.setTransformationEpsilon(1e-8);
    icp.setEuclideanFitnessEpsilon(1e-6);
    icp.align(*cloud_source_registered);

    // Visualize registration result
    pcl::visualization::PCLVisualizer viewer2("Registration Result");
    viewer2.addPointCloud(cloud_source_registered, "cloud_source_registered");
    viewer2.addPointCloud(cloud_target, "cloud_target");
    viewer2.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1, 0, 0, "cloud_source_registered");
    viewer2.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0, 1, 0, "cloud_target");
    viewer2.spin();

    return 0;
}

以上代码实现了以下功能：

加载源点云和目标点云；
计算点云表面法向量和FPFH特征；
找到特征点之间的对应关系；
根据距离和角度过滤对应关系；
可视化对应关系；
使用ICP算法进行点云配准；
可视化配准结果。

需要注意的是，以上代码中的ICP算法使用了BackProjection方式的对应关系估计方法，这种方法可以提高ICP算法的收敛速度和精度。如果需要更高的精度，可以使用NDT算法代替ICP算法进行配准