PCL汉明距离匹配代码

以下是使用PCL进行汉明距离匹配的示例代码：

#include <pcl/features/fpfh.h>
#include <pcl/features/normal_3d.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/registration/correspondence_estimation.h>
#include <pcl/registration/correspondence_rejection_distance.h>
#include <pcl/registration/correspondence_rejection_one_to_one.h>
#include <pcl/registration/correspondence_rejection_sample_consensus.h>
#include <pcl/registration/icp.h>
#include <pcl/registration/ia_ransac.h>
#include <pcl/common/transforms.h>
#include <iostream>

int main(int argc, char** argv)
{
    // Load point clouds
    pcl::PointCloud<pcl::PointXYZRGB>::Ptr source_cloud(new pcl::PointCloud<pcl::PointXYZRGB>);
    pcl::PointCloud<pcl::PointXYZRGB>::Ptr target_cloud(new pcl::PointCloud<pcl::PointXYZRGB>);
    pcl::io::loadPCDFile<pcl::PointXYZRGB>("source_cloud.pcd", *source_cloud);
    pcl::io::loadPCDFile<pcl::PointXYZRGB>("target_cloud.pcd", *target_cloud);

    // Estimate surface normals and FPFH descriptors
    pcl::PointCloud<pcl::Normal>::Ptr source_normals(new pcl::PointCloud<pcl::Normal>);
    pcl::PointCloud<pcl::Normal>::Ptr target_normals(new pcl::PointCloud<pcl::Normal>);
    pcl::search::KdTree<pcl::PointXYZRGB>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZRGB>);
    pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> ne;
    ne.setSearchMethod(tree);
    ne.setInputCloud(source_cloud);
    ne.setRadiusSearch(0.05);
    ne.compute(*source_normals);
    ne.setInputCloud(target_cloud);
    ne.compute(*target_normals);
    pcl::FPFHEstimation<pcl::PointXYZRGB, pcl::Normal, pcl::FPFHSignature33> fpfh;
    fpfh.setSearchMethod(tree);
    fpfh.setInputCloud(source_cloud);
    fpfh.setInputNormals(source_normals);
    fpfh.setRadiusSearch(0.05);
    pcl::PointCloud<pcl::FPFHSignature33>::Ptr source_features(new pcl::PointCloud<pcl::FPFHSignature33>);
    pcl::PointCloud<pcl::FPFHSignature33>::Ptr target_features(new pcl::PointCloud<pcl::FPFHSignature33>);
    fpfh.compute(*source_features);
    fpfh.setInputCloud(target_cloud);
    fpfh.setInputNormals(target_normals);
    fpfh.compute(*target_features);

    // Find correspondences using Hamming distance
    pcl::registration::CorrespondenceEstimation<pcl::FPFHSignature33, pcl::FPFHSignature33> est;
    est.setInputSource(source_features);
    est.setInputTarget(target_features);
    pcl::CorrespondencesPtr correspondences(new pcl::Correspondences);
    est.determineCorrespondences(*correspondences);

    // Reject correspondences based on distance
    pcl::registration::CorrespondenceRejectorDistance rej_distance;
    rej_distance.setInputCorrespondences(correspondences);
    rej_distance.setMaximumDistance(0.05);
    pcl::CorrespondencesPtr correspondences_distance(new pcl::Correspondences);
    rej_distance.getCorrespondences(*correspondences_distance);

    // Reject correspondences based on one-to-one matching
    pcl::registration::CorrespondenceRejectorOneToOne rej_one_to_one;
    rej_one_to_one.setInputCorrespondences(correspondences_distance);
    pcl::CorrespondencesPtr correspondences_one_to_one(new pcl::Correspondences);
    rej_one_to_one.getCorrespondences(*correspondences_one_to_one);

    // Perform ICP registration
    pcl::IterativeClosestPoint<pcl::PointXYZRGB, pcl::PointXYZRGB> icp;
    icp.setMaxCorrespondenceDistance(0.05);
    icp.setMaximumIterations(100);
    icp.setInputSource(source_cloud);
    icp.setInputTarget(target_cloud);
    icp.setCorrespondences(correspondences_one_to_one);
    pcl::PointCloud<pcl::PointXYZRGB>::Ptr aligned_cloud(new pcl::PointCloud<pcl::PointXYZRGB>);
    icp.align(*aligned_cloud);

    // Print transformation matrix
    std::cout << "Transformation matrix:" << std::endl << icp.getFinalTransformation() << std::endl;

    // Visualize results
    pcl::visualization::PCLVisualizer viewer("ICP Registration");
    viewer.addPointCloud(target_cloud, "target_cloud");
    viewer.addPointCloud(source_cloud, "source_cloud");
    viewer.addPointCloud(aligned_cloud, "aligned_cloud");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 0.0, 0.0, "source_cloud");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0.0, 1.0, 0.0, "target_cloud");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0.0, 0.0, 1.0, "aligned_cloud");
    viewer.spin();

    return 0;
}

在上述代码中，我们首先加载了源点云和目标点云，并对其进行了特征提取。然后，我们使用CorrespondenceEstimation类计算了两个点云之间的特征匹配，并使用CorrespondenceRejectorDistance和CorrespondenceRejectorOneToOne类对匹配结果进行了筛选。最后，我们使用IterativeClosestPoint类进行了ICP配准，并将结果可视化展示出来。在特征匹配中，我们使用了默认的汉明距离算法。