我一直在尝试使用pcl工具将圆柱体模型拟合到生成的点云上，我一直在修改here文档中提供的示例代码。
据我所知，pcl::SampleConsensusModelCylinder需要法线数据，所以我在代码中添加了一个新的检查，用于检查-cf参数。然后，代码计算pcl::Normals类型点云中每个点的法线（称为cloud_normals），并将其与原始pcl::PointXYZ类型点云连接。我将其保存到称为cloud_normalpoints的新的pcl::PointNormal类型的点云，并且利用该点云，我尝试使用随机采样一致性（RANSAC）来拟合pcl::SampleConsensusModelCylinder。
我已经包含了下面的代码：

else if (pcl::console::find_argument (argc, argv, "-cf") >= 0 )
  {
    //TODO: find fastest way to fit cylinder model to point cloud
    // the cloud_normals point cloud will be used to store the point cloud or normals
    pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
    cloud_normals->width    = 500;
    cloud_normals->height   = 1;
    // is_dense is True if no points have NaN or Inf in any of their floating points field
    cloud_normals->is_dense = false;
    cloud_normals->points.resize (cloud_normals->width * cloud_normals->height);

    // the NormalEstimation object ne is created and will estimate the normals and curvature at each point
    pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
    // the search:KdTree object pointer points to a search method for finding points in 3D space (3D point clouds)
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
    // use the filtered cloud as an input to the normal estimator
    ne.setInputCloud (cloud);
    // set number of k-nearest neighbours to use for feature estimation to 50
    ne.setKSearch (50);
    // compute normals and save these to the clouds_normals point cloud
    ne.compute (*cloud_normals);

    pcl::PointCloud<pcl::PointNormal>::Ptr cloud_normalpoints (new pcl::PointCloud<pcl::PointNormal>);
    cloud_normalpoints->width    = 500;
    cloud_normalpoints->height   = 1;
    // is_dense is True if no points have NaN or Inf in any of their floating points field
    cloud_normalpoints->is_dense = false;
    cloud_normalpoints->points.resize (cloud_normalpoints->width * cloud_normalpoints->height);

    pcl::concatenateFields(*cloud,*cloud_normals,*cloud_normalpoints);

    //TODO: Solve normals not given error
    pcl::SampleConsensusModelCylinder<pcl::PointNormal, pcl::Normal>::Ptr
      model_c (new pcl::SampleConsensusModelCylinder<pcl::PointNormal, pcl::Normal> (cloud_normalpoints));
    // Declares ransac as a ransac implementation searching for a cylinder (according to model_c -> in cloud)
    pcl::RandomSampleConsensus<pcl::PointNormal> ransac (model_c);
    // Set distance threshold of .01 -> believe this is for inliers
    ransac.setDistanceThreshold (.01);
    // Compute model coefficients and find inliers
    ransac.computeModel();
    
    // Return indices of best set of inliers so far for this model
    ransac.getInliers(inliers);
  }

我还添加了一些代码来生成一个包含圆柱体的原始点云，但这是可行的，所以我不会在这里详细介绍。
当我运行我的代码时，它进入计算模型阶段，然后抛出以下错误：
[pcl::SampleConsensusModelCylinder::computeModelCoefficients] No input dataset containing normals was given!
有人知道这是为什么吗？cloud_normalpoints云包括了每个点的正常数据。我应该设置不同的RANSAC估计器吗？我应该使用不同的点类型吗？我对pcl比较陌生，所以任何帮助都将不胜感激！