PCL汉明距离特征匹配代码

下面是使用PCL进行汉明距离特征匹配的示例代码：

#include <pcl/io/pcd_io.h>
#include <pcl/features/fpfh.h>
#include <pcl/features/shot_omp.h>
#include <pcl/search/flann_search.h>
#include <pcl/registration/correspondence_estimation.h>
#include <pcl/registration/correspondence_rejection_sample_consensus.h>

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

    // 提取SHOT描述子
    pcl::SHOTEstimationOMP<pcl::PointXYZ, pcl::SHOT352> shot;
    pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
    shot.setSearchMethod(kdtree);
    shot.setRadiusSearch(0.05);
    pcl::PointCloud<pcl::SHOT352>::Ptr target_descriptors(new pcl::PointCloud<pcl::SHOT352>);
    pcl::PointCloud<pcl::SHOT352>::Ptr source_descriptors(new pcl::PointCloud<pcl::SHOT352>);
    shot.setInputCloud(target_cloud);
    shot.compute(*target_descriptors);
    shot.setInputCloud(source_cloud);
    shot.compute(*source_descriptors);

    // 将SHOT描述子转换为二进制描述子
    pcl::PointCloud<pcl::SHOT352>::Ptr target_binary_descriptors(new pcl::PointCloud<pcl::SHOT352>);
    pcl::PointCloud<pcl::SHOT352>::Ptr source_binary_descriptors(new pcl::PointCloud<pcl::SHOT352>);
    pcl::SHOTColorEstimation<pcl::PointXYZ, pcl::Normal, pcl::SHOT352> color;
    color.setInputCloud(target_cloud);
    color.setInputNormals(target_normals);
    color.setSearchMethod(kdtree);
    color.setRadiusSearch(0.05);
    color.compute(*target_binary_descriptors);
    color.setInputCloud(source_cloud);
    color.setInputNormals(source_normals);
    color.compute(*source_binary_descriptors);

    // 使用FLANN库进行最近邻搜索
    pcl::CorrespondencesPtr correspondences(new pcl::Correspondences);
    pcl::FlannSearch<pcl::SHOT352> search;
    search.setInputCloud(source_binary_descriptors);
    for (size_t i = 0; i < target_binary_descriptors->size(); i++) {
        std::vector<int> indices(1);
        std::vector<float> distances(1);
        search.nearestKSearch(target_binary_descriptors->at(i), 1, indices, distances);
        if (distances[0] < 0.25) {
            pcl::Correspondence correspondence(i, indices[0], distances[0]);
            correspondences->push_back(correspondence);
        }
    }

    // 使用RANSAC进行匹配剔除和配准
    pcl::registration::CorrespondenceRejectorSampleConsensus<pcl::PointXYZ> sac;
    sac.setInputSource(source_cloud);
    sac.setInputTarget(target_cloud);
    sac.setInlierThreshold(0.01);
    sac.setMaximumIterations(10000);
    pcl::CorrespondencesPtr inliers(new pcl::Correspondences);
    sac.getRemainingCorrespondences(*correspondences, *inliers);
    pcl::registration::TransformationEstimationSVD<pcl::PointXYZ, pcl::PointXYZ> estimation;
    Eigen::Matrix4f transform;
    estimation.estimateRigidTransformation(*source_cloud, *target_cloud, *inliers, transform);
    pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::transformPointCloud(*source_cloud, *transformed_cloud, transform);

    return 0;
}

以上代码仅供参考，具体实现可能会有所不同。在实际使用中，还需要根据具体情况进行参数调整和优化。