PCL汉明距离特征匹配点对代码

以下是使用 PCL 库实现的汉明距离特征匹配点对的示例代码：

#include <pcl/features/fpfh.h>
#include <pcl/features/normal_3d_omp.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/correspondence_rejection_sample_consensus.h>
#include <pcl/registration/icp.h>
#include <pcl/visualization/pcl_visualizer.h>

typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloudT;
typedef pcl::FPFHSignature33 FeatureT;
typedef pcl::PointCloud<FeatureT> FeatureCloudT;

int main(int argc, char** argv)
{
    // 加载点云数据
    PointCloudT::Ptr source_cloud(new PointCloudT);
    PointCloudT::Ptr target_cloud(new PointCloudT);
    pcl::io::loadPCDFile("source.pcd", *source_cloud);
    pcl::io::loadPCDFile("target.pcd", *target_cloud);

    // 计算法向量
    pcl::NormalEstimationOMP<PointT, pcl::Normal> ne;
    ne.setNumberOfThreads(8);
    ne.setInputCloud(source_cloud);
    pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>());
    ne.setSearchMethod(tree);
    pcl::PointCloud<pcl::Normal>::Ptr source_normals(new pcl::PointCloud<pcl::Normal>);
    ne.setRadiusSearch(0.03);
    ne.compute(*source_normals);
    ne.setInputCloud(target_cloud);
    pcl::PointCloud<pcl::Normal>::Ptr target_normals(new pcl::PointCloud<pcl::Normal>);
    ne.compute(*target_normals);

    // 计算特征向量
    pcl::FPFHEstimation<PointT, pcl::Normal, FeatureT> fe;
    fe.setInputCloud(source_cloud);
    fe.setInputNormals(source_normals);
    fe.setSearchMethod(tree);
    FeatureCloudT::Ptr source_features(new FeatureCloudT);
    fe.setRadiusSearch(0.05);
    fe.compute(*source_features);
    fe.setInputCloud(target_cloud);
    fe.setInputNormals(target_normals);
    FeatureCloudT::Ptr target_features(new FeatureCloudT);
    fe.compute(*target_features);

    // 找到匹配点对
    pcl::CorrespondencesPtr correspondences(new pcl::Correspondences);
    for (size_t i = 0; i < source_features->size(); ++i)
    {
        int k = -1;
        float min_distance = std::numeric_limits<float>::max();
        for (size_t j = 0; j < target_features->size(); ++j)
        {
            float distance = 0;
            for (int k = 0; k < 33; ++k)
            {
                if (source_features->points[i].histogram[k] != target_features->points[j].histogram[k])
                {
                    distance++;
                }
            }
            if (distance < min_distance)
            {
                min_distance = distance;
                k = j;
            }
        }
        if (k >= 0)
        {
            correspondences->push_back(pcl::Correspondence(i, k, min_distance));
        }
    }

    // 根据匹配点对进行 ICP 变换
    pcl::IterativeClosestPoint<PointT, PointT> icp;
    icp.setInputSource(source_cloud);
    icp.setInputTarget(target_cloud);
    icp.setMaxCorrespondenceDistance(0.05);
    pcl::CorrespondencesPtr correspondences_filtered(new pcl::Correspondences);
    pcl::registration::CorrespondenceRejectorSampleConsensus<PointT>::Ptr ransac(new pcl::registration::CorrespondenceRejectorSampleConsensus<PointT>());
    ransac->setInputSource(source_cloud);
    ransac->setInputTarget(target_cloud);
    ransac->setInlierThreshold(0.05);
    ransac->setMaximumIterations(1000);
    ransac->setInputCorrespondences(correspondences);
    ransac->getCorrespondences(*correspondences_filtered);
    icp.setCorrespondences(correspondences_filtered);
    PointCloudT::Ptr aligned_cloud(new PointCloudT);
    icp.align(*aligned_cloud);

    // 可视化结果
    pcl::visualization::PCLVisualizer viewer("PCL Viewer");
    viewer.addPointCloud(source_cloud, "source_cloud");
    viewer.addPointCloud(target_cloud, "target_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;
}

以上代码实现了通过汉明距离计算特征匹配点对，并使用 ICP 算法进行配准的功能。在代码中，我们首先使用 PCL 库计算点云数据的法向量和特征向量，然后遍历所有的特征向量，计算它们之间的汉明距离，并找到它们之间的匹配点对。最后，使用 ICP 算法将源点云和目标点云进行配准，并将结果可视化展示出来。