PCL汉明距离特征点匹配代码

以下是使用 PCL 进行汉明距离特征点匹配的示例代码：

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud1(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud2(new pcl::PointCloud<pcl::PointXYZ>);

// 读入点云数据
pcl::io::loadPCDFile("cloud1.pcd", *cloud1);
pcl::io::loadPCDFile("cloud2.pcd", *cloud2);

// 特征点提取
pcl::HarrisKeypoint3D<pcl::PointXYZ, pcl::PointXYZI> harris;
pcl::PointCloud<pcl::PointXYZI>::Ptr keypoints1(new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointCloud<pcl::PointXYZI>::Ptr keypoints2(new pcl::PointCloud<pcl::PointXYZI>);
harris.setInputCloud(cloud1);
harris.setNonMaxSupression(true);
harris.setRadius(0.05);
harris.compute(*keypoints1);
harris.setInputCloud(cloud2);
harris.compute(*keypoints2);

// 描述符计算
pcl::SHOTColorEstimation<pcl::PointXYZ, pcl::PointXYZI, pcl::SHOT1344> shot;
pcl::PointCloud<pcl::SHOT1344>::Ptr descriptors1(new pcl::PointCloud<pcl::SHOT1344>);
pcl::PointCloud<pcl::SHOT1344>::Ptr descriptors2(new pcl::PointCloud<pcl::SHOT1344>);
shot.setInputCloud(cloud1);
shot.setInputNormals(cloud1);
shot.setRadiusSearch(0.1);
shot.setInputCloud(keypoints1);
shot.compute(*descriptors1);
shot.setInputCloud(cloud2);
shot.setInputNormals(cloud2);
shot.setInputCloud(keypoints2);
shot.compute(*descriptors2);

// 特征点匹配
pcl::CorrespondencesPtr correspondences(new pcl::Correspondences);
for (size_t i = 0; i < keypoints1->size(); ++i)
{
    int min_index = -1;
    float min_distance = std::numeric_limits<float>::max();
    for (size_t j = 0; j < keypoints2->size(); ++j)
    {
        float distance = pcl::getHammingDistance((*descriptors1)[i].descriptor, (*descriptors2)[j].descriptor);
        if (distance < min_distance)
        {
            min_distance = distance;
            min_index = j;
        }
    }
    if (min_index >= 0)
    {
        pcl::Correspondence correspondence(i, min_index, min_distance);
        correspondences->push_back(correspondence);
    }
}

// 输出匹配结果
std::cout << "Found " << correspondences->size() << " correspondences." << std::endl;

该代码中使用了 PCL 中的 HarrisKeypoint3D 和 SHOTColorEstimation 进行特征点的提取和描述符的计算，然后使用 getHammingDistance 函数计算汉明距离，并将最小距离的点对作为匹配结果。