PCL汉明距离特征点匹配代码

以下是使用PCL库进行汉明距离特征点匹配的示例代码：

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/features/fpfh.h>
#include <pcl/features/normal_3d.h>
#include <pcl/registration/correspondence_estimation.h>
#include <pcl/registration/correspondence_rejection_features.h>
#include <pcl/registration/correspondence_rejection_sample_consensus.h>
#include <pcl/registration/icp.h>

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

    // Compute surface normals and FPFH features
    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::PointXYZ>::Ptr search_tree(new pcl::search::KdTree<pcl::PointXYZ>);
    pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;
    normal_estimator.setInputCloud(source_cloud);
    normal_estimator.setSearchMethod(search_tree);
    normal_estimator.setRadiusSearch(0.03);
    normal_estimator.compute(*source_normals);
    normal_estimator.setInputCloud(target_cloud);
    normal_estimator.compute(*target_normals);
    pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh_estimator;
    fpfh_estimator.setInputCloud(source_cloud);
    fpfh_estimator.setInputNormals(source_normals);
    fpfh_estimator.setSearchMethod(search_tree);
    fpfh_estimator.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_estimator.compute(*source_features);
    fpfh_estimator.setInputCloud(target_cloud);
    fpfh_estimator.setInputNormals(target_normals);
    fpfh_estimator.compute(*target_features);

    // Compute correspondences between the feature clouds
    pcl::registration::CorrespondenceEstimation<pcl::FPFHSignature33, pcl::FPFHSignature33> correspondence_estimator;
    correspondence_estimator.setInputSource(source_features);
    correspondence_estimator.setInputTarget(target_features);
    pcl::CorrespondencesPtr correspondences(new pcl::Correspondences);
    correspondence_estimator.determineCorrespondences(*correspondences);

    // Reject correspondences based on feature distance
    pcl::registration::CorrespondenceRejectorFeatures<pcl::FPFHSignature33> correspondence_rejector_features;
    correspondence_rejector_features.setInputSource(source_features);
    correspondence_rejector_features.setInputTarget(target_features);
    correspondence_rejector_features.setThreshold(0.2);
    correspondence_rejector_features.getRemainingCorrespondences(*correspondences);

    // Reject correspondences based on RANSAC
    pcl::registration::CorrespondenceRejectorSampleConsensus<pcl::PointXYZ> correspondence_rejector_ransac;
    correspondence_rejector_ransac.setInputSource(source_cloud);
    correspondence_rejector_ransac.setInputTarget(target_cloud);
    correspondence_rejector_ransac.setInlierThreshold(0.01);
    correspondence_rejector_ransac.setMaximumIterations(10000);
    correspondence_rejector_ransac.setInputCorrespondences(correspondences);
    correspondence_rejector_ransac.getRemainingCorrespondences(*correspondences);

    // Compute transformation matrix using ICP
    pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
    icp.setInputSource(source_cloud);
    icp.setInputTarget(target_cloud);
    icp.setMaxCorrespondenceDistance(0.05);
    icp.setMaximumIterations(100);
    icp.setTransformationEpsilon(1e-8);
    icp.setEuclideanFitnessEpsilon(1e-6);
    icp.setInputCorrespondences(correspondences);
    pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud(new pcl::PointCloud<pcl::PointXYZ>);
    icp.align(*transformed_cloud);

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

    return 0;
}

其中，source.pcd 和 target.pcd 分别是需要匹配的点云文件，0.03、0.05、0.2、0.01、0.05、100 和 1e-8、1e-6 分别是用于计算特征和匹配的参数，可以根据实际情况进行调整。