pcl里面使用KdTree来搜索

下面这个教程我们将学会怎么用KdTree找一个特殊点附近的K个最近邻，然后我们也将复习怎么通过一个特殊的半径来找里面所有的近邻。

一个k-d树，或者k维的树是一个计算机科学里面的数据结构。它是一个有其它约束影响的二叉搜索树。K-d树是在深度和最近邻搜索里面很有用的。我们这次的目的是生成一个3维的k-d trees。一个k-d tree的每个层次在某个维度上分割成所有的子树，使用一个垂直于相应坐标轴的高维平面。在树的根部，所有的子树将会被分割以第一维(如果第一维坐标系比根部少，它将会成为左子树，如果比根部多，它将会成为右子树)。每一层的树将会在下一层进行分叉，它会跳转到第一层如果全部都分完了。最有效的去建立k-d tree的方法是使用一个分割的方法，就像快速排序。你可以在你的左子树和右子树上重复这一过程，直到最后一个你要去分割的树只有一个元素。

2维的k-d树

下面是一个最近邻搜索的工作

 

 

代码

#include <pcl/point_cloud.h>
#include <pcl/kdtree/kdtree_flann.h>

#include <iostream>
#include <vector>
#include <ctime>

int
main (int argc, char** argv)
{
  srand (time (NULL));

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);

  // Generate pointcloud data
  cloud->width = 1000;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024.0f * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024.0f * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024.0f * rand () / (RAND_MAX + 1.0f);
  }

  pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;

  kdtree.setInputCloud (cloud);

  pcl::PointXYZ searchPoint;

  searchPoint.x = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.y = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.z = 1024.0f * rand () / (RAND_MAX + 1.0f);

  // K nearest neighbor search

  int K = 10;

  std::vector<int> pointIdxNKNSearch(K);
  std::vector<float> pointNKNSquaredDistance(K);

  std::cout << "K nearest neighbor search at (" << searchPoint.x 
            << " " << searchPoint.y 
            << " " << searchPoint.z
            << ") with K=" << K << std::endl;

  if ( kdtree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0 )
  {
    for (size_t i = 0; i < pointIdxNKNSearch.size (); ++i)
      std::cout << "    "  <<   cloud->points[ pointIdxNKNSearch[i] ].x 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].y 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].z 
                << " (squared distance: " << pointNKNSquaredDistance[i] << ")" << std::endl;
  }

  // Neighbors within radius search

  std::vector<int> pointIdxRadiusSearch;
  std::vector<float> pointRadiusSquaredDistance;

  float radius = 256.0f * rand () / (RAND_MAX + 1.0f);

  std::cout << "Neighbors within radius search at (" << searchPoint.x 
            << " " << searchPoint.y 
            << " " << searchPoint.z
            << ") with radius=" << radius << std::endl;


  if ( kdtree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0 )
  {
    for (size_t i = 0; i < pointIdxRadiusSearch.size (); ++i)
      std::cout << "    "  <<   cloud->points[ pointIdxRadiusSearch[i] ].x 
                << " " << cloud->points[ pointIdxRadiusSearch[i] ].y 
                << " " << cloud->points[ pointIdxRadiusSearch[i] ].z 
                << " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << std::endl;
  }


  return 0;
}

下面的代码是创造了kdtree这个对象，并把我们随机生成的点云作为输入。

  pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;

  kdtree.setInputCloud (cloud);

  pcl::PointXYZ searchPoint;

  searchPoint.x = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.y = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.z = 1024.0f * rand () / (RAND_MAX + 1.0f);

我们接下去创造了一个整数(通常设置为10)和两个向量存储搜索后的K个最近邻。

  // K nearest neighbor search

  int K = 10;

  std::vector<int> pointIdxNKNSearch(K);
  std::vector<float> pointNKNSquaredDistance(K);

  std::cout << "K nearest neighbor search at (" << searchPoint.x 
            << " " << searchPoint.y 
            << " " << searchPoint.z
            << ") with K=" << K << std::endl;

假设我们的kdtree返回了大于0个近邻。那么它将打印出在我们"searchPoint"附近的10个最近的邻居并把它们存到先前创立的向量中。

  if ( kdtree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0 )
  {
    for (size_t i = 0; i < pointIdxNKNSearch.size (); ++i)
      std::cout << "    "  <<   cloud->points[ pointIdxNKNSearch[i] ].x 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].y 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].z 
                << " (squared distance: " << pointNKNSquaredDistance[i] << ")" << std::endl;
  }

 // Neighbors within radius search

  std::vector<int> pointIdxRadiusSearch;
  std::vector<float> pointRadiusSquaredDistance;

  float radius = 256.0f * rand () / (RAND_MAX + 1.0f);

我们把向量里面的点打印出来

  if ( kdtree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0 )
  {
    for (size_t i = 0; i < pointIdxRadiusSearch.size (); ++i)
      std::cout << "    "  <<   cloud->points[ pointIdxRadiusSearch[i] ].x 
                << " " << cloud->points[ pointIdxRadiusSearch[i] ].y 
                << " " << cloud->points[ pointIdxRadiusSearch[i] ].z 
                << " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << std::endl;
  }

结果

K nearest neighbor search at (455.807 417.256 406.502) with K=10
  494.728 371.875 351.687 (squared distance: 6578.99)
  506.066 420.079 478.278 (squared distance: 7685.67)
  368.546 427.623 416.388 (squared distance: 7819.75)
  474.832 383.041 323.293 (squared distance: 8456.34)
  470.992 334.084 468.459 (squared distance: 10986.9)
  560.884 417.637 364.518 (squared distance: 12803.8)
  466.703 475.716 306.269 (squared distance: 13582.9)
  456.907 336.035 304.529 (squared distance: 16996.7)
  452.288 387.943 279.481 (squared distance: 17005.9)
  476.642 410.422 268.057 (squared distance: 19647.9)
Neighbors within radius search at (455.807 417.256 406.502) with radius=225.932
  494.728 371.875 351.687 (squared distance: 6578.99)
  506.066 420.079 478.278 (squared distance: 7685.67)
  368.546 427.623 416.388 (squared distance: 7819.75)
  474.832 383.041 323.293 (squared distance: 8456.34)
  470.992 334.084 468.459 (squared distance: 10986.9)
  560.884 417.637 364.518 (squared distance: 12803.8)
  466.703 475.716 306.269 (squared distance: 13582.9)
  456.907 336.035 304.529 (squared distance: 16996.7)
  452.288 387.943 279.481 (squared distance: 17005.9)
  476.642 410.422 268.057 (squared distance: 19647.9)
  499.429 541.532 351.35 (squared distance: 20389)
  574.418 452.961 334.7 (squared distance: 20498.9)
  336.785 391.057 488.71 (squared distance: 21611)
  319.765 406.187 350.955 (squared distance: 21715.6)
  528.89 289.583 378.979 (squared distance: 22399.1)
  504.509 459.609 541.732 (squared distance: 22452.8)
  539.854 349.333 300.395 (squared distance: 22936.3)
  548.51 458.035 292.812 (squared distance: 23182.1)
  546.284 426.67 535.989 (squared distance: 25041.6)
  577.058 390.276 508.597 (squared distance: 25853.1)
  543.16 458.727 276.859 (squared distance: 26157.5)
  613.997 387.397 443.207 (squared distance: 27262.7)
  608.235 467.363 327.264 (squared distance: 32023.6)
  506.842 591.736 391.923 (squared distance: 33260.3)
  529.842 475.715 241.532 (squared distance: 36113.7)
  485.822 322.623 244.347 (squared distance: 36150.5)
  362.036 318.014 269.201 (squared distance: 37493.6)
  493.806 600.083 462.742 (squared distance: 38032.3)
  392.315 368.085 585.37 (squared distance: 38442.9)
  303.826 428.659 533.642 (squared distance: 39392.8)
  616.492 424.551 289.524 (squared distance: 39556.8)
  320.563 333.216 278.242 (squared distance: 41804.5)
  646.599 502.256 424.46 (squared distance: 43948.8)
  556.202 325.013 568.252 (squared distance: 44751)
  291.27 497.352 515.938 (squared distance: 45463.9)
  286.483 322.401 495.377 (squared distance: 45567.2)
  367.288 550.421 550.551 (squared distance: 46318.6)
  595.122 582.77 394.894 (squared distance: 46938.1)
  256.784 499.401 379.931 (squared distance: 47064.1)
  430.782 230.854 293.829 (squared distance: 48067.2)
  261.051 486.593 329.854 (squared distance: 48612.7)
  602.061 327.892 545.269 (squared distance: 48632.4)
  347.074 610.994 395.622 (squared distance: 49475.6)
  482.876 284.894 583.888 (squared distance: 49718.6)
  356.962 247.285 514.959 (squared distance: 50423.7)
  282.065 509.488 516.216 (squared distance: 50730.4)