小编lot*_*ine的帖子

我尝试用PCL执行ICP,

但 pcl::transformPointCloud不起作用.这是我的代码:

int
 main ()
{
   pcl::PointCloud<pcl::PointXYZI>::Ptr cloudIn (new pcl::PointCloud<pcl::PointXYZI>);
   pcl::PointCloud<pcl::PointXYZI>::Ptr cloudOut (new pcl::PointCloud<pcl::PointXYZI>);
   pcl::PointCloud<pcl::PointXYZI> finalCloud ;
   pcl::PointCloud<pcl::PointXYZI> finalCloud1 ;
   pcl::PointCloud<pcl::PointXYZI>::Ptr cloudOut_new (new pcl::PointCloud<pcl::PointXYZI>) ;

   if(pcl::io::loadPCDFile ("/ICP_PCL/000.pcd", *cloudIn)==-1)
   {
     PCL_ERROR ("Couldn't read first file! \n");
     return (-1);
   }

   if(pcl::io::loadPCDFile ("/ICP_PCL/001.pcd", *cloudOut)==-1)
   {
     PCL_ERROR ("Couldn't read second input file! \n");
     return (-1);
   }

  pcl::IterativeClosestPoint<pcl::PointXYZI, pcl::PointXYZI> icp;
  icp.setInputCloud(cloudOut);
  icp.setInputTarget(cloudIn);
  icp.setMaximumIterations (500);
  icp.setTransformationEpsilon (1e-9);
  icp.setMaxCorrespondenceDistance (0.05);
  icp.setEuclideanFitnessEpsilon (1);
  icp.setRANSACOutlierRejectionThreshold (1.5);

  icp.align(finalCloud);

  if (icp.hasConverged())
  {
      std::cout << "ICP converged." << std::endl
                << …

我正在用某个对象填充 std::vector。

此向量实例定义在以下行中：

std::vector< std::list< pcl::PointXYZRGB>> tab;

我现在想删除空单元格。我试过如下：

tab.erase(remove_if(tab.begin(), tab.end(), std::is_empty), tab.end());

我收到以下错误：

error: missing template arguments before ‘)’ token
     voxels.erase(remove_if(voxels.begin(), voxels.end(), is_empty**)**, voxels.end());

我很困惑，有人能告诉我怎么做吗？

我有一个点云。我想得到它的RGB值。我怎样才能做到这一点？
\n 为了让我的问题更清楚，请查看代码。

// Load the first input file into a PointCloud<T> with an appropriate type : \n        pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud1 (new pcl::PointCloud<pcl::PointXYZRGB>);\n        if (pcl::io::loadPCDFile<pcl::PointXYZRGB> ("../data/station1.pcd", *cloud1) == -1)\n        {\n            std::cout << "Error reading PCD file !!!" << std::endl;\n            exit(-1);\n        }\n

我想单独获取每个值

std::cout << " x = " << cloud1->points[11].x << std::endl;\nstd::cout << " y = " << cloud1->points[11].y << std::endl;\nstd::cout << " z = " << cloud1->points[11].z << std::endl;\nstd::cout << " r = " << cloud1->points[11].r << …

我的问题是：我有 3D 点云。我想将每个法线归因于每个点。来自 PCL 教程：

// Create the normal estimation class, and pass the input dataset to it
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setInputCloud (cloud.makeShared());

// Create an empty kdtree representation, and pass it to the normal estimation object.
// Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
ne.setSearchMethod (tree);

// Output datasets
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);

// Use all neighbors in a sphere …