PCL的kdtree查找的示例应用

亦凉 2023-07-07 11:28 2阅读 0赞

## 代码块讲解 ##

首先使用系统时间初始化rand()函数的种子，然后用随机数据填充点云对象

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);
      }

下边代码创建了KdTreeFLANN对象，并把创建的点云设置成输入，然后创建一个searchPoint作为查询点，并分配随机坐标

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);

现在创建一个整数（设置成10）和两个向量来存储搜索到的K近邻，两个向量中一个存储搜索到查询点近邻的索引，另一个存储对应近邻的距离平方

// 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kd-tree对象返回了多于0个近邻，搜索结果已经存储在两个向量中，并把所有十个近邻的位置打印输出

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;
      }

下面代码展示查找到给定searchPoint的某一半径内的所有近邻，重新定义两个向量来存储关于近邻的信息

std::vector<int> pointIdxRadiusSearch;
      std::vector<float> pointRadiusSquaredDistance;
    
      float radius = 256.0f * rand () / (RAND_MAX + 1.0f);

如果kd-tree对象在指定半径内返回多于0个近邻，打印向量储存值的坐标和距离

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;
      }

## 完整代码 ##

#include <iostream>
    #include <pcl/visualization/cloud_viewer.h>
    #include <pcl/io/io.h>
    #include <pcl/io/pcd_io.h>
    #include <pcl/point_types.h>
    #include <pcl/kdtree/kdtree_flann.h>
    #include <vector>
    
    
    
    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;
    }

## 运行结果 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1_size_16_color_FFFFFF_t_70][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200223114010335.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxNjg1MjY1,size_16,color_FFFFFF,t_70