点云PCL中小细节

计算点与点之间的距离的平局距离

double
computeCloudResolution (const pcl::PointCloud<PointType>::ConstPtr &cloud)
{
  double res = 0.0;
  int n_points = ;
  int nres;
  std::vector<int> indices ();
  std::vector<float> sqr_distances ();
  pcl::search::KdTree<PointType> tree;
  tree.setInputCloud (cloud);

  for (size_t i = ; i < cloud->size (); ++i)
  {
    if (! pcl_isfinite ((*cloud)[i].x))
    {
      continue;
    }
    //Considering the second neighbor since the first is the point itself.
    nres = tree.nearestKSearch (i, , indices, sqr_distances);
    if (nres == )
    {
      res += sqrt (sqr_distances[]);
      ++n_points;
    }
  }
  if (n_points != )
  {
    res /= n_points;
  }
  return res;
}

PCL中

对于 pcl::UniformSampling函数在PCL1.7版本里  该函数放在keypoints
用法如下：
pcl::PointCloud<int> keypointIndices;
filter.compute(keypointIndices);
pcl::copyPointCloud(*cloud,keypointIndices.points, *filteredCloud);

之后在PCL1.8版本里就将该函数放在filters模块里。并在keypoints模块里也包含了这个头文件
#warning UniformSampling is not a Keypoint anymore, use <pcl/filters/uniform_sampling.h> instead.
这是keypoints模块下的说明
用法是：
  PointCloud<PointXYZ> output_;
 filter.filter (output_);