點雲PCL中小細節

計算點與點之間的距離的平局距離

double
computeCloudResolution (const pcl::PointCloud<PointType>::ConstPtr &cloud)
{
  double res = 0.0;
  int n_points = 0;
  int nres;
  std::vector<int> indices (2);
  std::vector<float> sqr_distances (2);
  pcl::search::KdTree<PointType> tree;
  tree.setInputCloud (cloud);

  for (size_t i = 0; 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, 2, indices, sqr_distances);
    if (nres == 2)
    {
      res += sqrt (sqr_distances[1]);
      ++n_points;
    }
  }
  if (n_points != 0)
  {
    res /= n_points;
  }
  return res;
}

PCL中

對於 pcl::UniformSampling函數在PCL1.7版本里  該函數放在keypoints
用法如下：
pcl::PointCloud<int> keypointIndices;
filter.compute(keypointIndices);
pcl::copyPointCloud(*cloud, keypointIndices.points, *filteredCloud);

之后在PCL1.8版本里就將該函數放在filters模塊里。並在keypoints模塊里也包含了這個頭文件
#warning UniformSampling is not a Keypoint anymore, use <pcl/filters/uniform_sampling.h> instead.
這是keypoints模塊下的說明
用法是：
  PointCloud<PointXYZ> output_;
  filter.filter (output_);