PCL库学习笔记（Octree寻找邻近点及其应用）-白红宇

PCL库学习笔记（Octree寻找邻近点及其应用）

阅读量：4049 次

发布时间：2019-05-25

本文共 2083 字，大约阅读时间需要 6 分钟。

上文我们利用了kdtree进行邻近点的提取。在点云pcl库中，还有一个重要的点查找方法就是octree。其原理和应用在这篇博文中有了比较详细的介绍：

octree有三种寻找最近邻的方式分别为：
1、neighbors within voxel search 就是返回该点所在的voxel中的所有其他点的索引；
2、K nearest Neighbor search 就是返回该点最近的K个点；
3、Neighbors within radius search 就是返回限定半径范围内的所有点；
下面代码只输出了三种情况的前10个点，可以发现其实找的点还是有存在差异的。具体哪种情况用哪一种方式，还是需要具体分析。后期可能会补充三种方式在邻近点寻找上的可视化展示，方便更好理解。

#include
   
    #include
    
     #include
     
      #include
      
       #include
       
        #include
        
         using namespace std;int main(){ pcl::PointCloud
         
          ::Ptr cloud(new pcl::PointCloud
          
           ); cloud->height = 100000; cloud->width = 1; cloud->is_dense = true; cloud->resize(cloud->width*cloud->height); for (size_t i = 0; i < cloud->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); } // construct octree float resolution = 128.0f; pcl::octree::OctreePointCloudSearch
           
             octree(resolution); octree.setInputCloud(cloud); octree.addPointsFromInputCloud(); // set search point pcl::PointXYZ searchPoint; searchPoint = cloud->points[10]; // neighbors within voxel search vector
            
              Idx1; octree.voxelSearch(searchPoint, Idx1); cout << "Neighbors within voxel search points:\n"; for (size_t i = 0; i < 10; i++) { cout << cloud->points[Idx1[i]].x << " " << cloud->points[Idx1[i]].y << " " << cloud->points[Idx1[i]].z << endl; } // K nearest Neighbor search int K = Idx1.size(); vector
             
               Idx2; vector
              
                Distance1; octree.nearestKSearch(searchPoint, K, Idx2, Distance1); cout << "\nK nearest Neighbor search points:\n"; for (size_t i = 0; i < 10; i++) { cout << cloud->points[Idx2[i]].x << " " << cloud->points[Idx2[i]].y << " " << cloud->points[Idx2[i]].z << endl; } // Neighbors within radius search float radius = resolution; vector
               
                 Idx3; vector
                
                  Distance2; octree.radiusSearch(searchPoint, radius, Idx3, Distance2); cout << "\nNeighbors within radius search points:\n"; for (size_t i = 0; i < 10; i++) { cout << cloud->points[Idx3[i]].x << " " << cloud->points[Idx3[i]].y << " " << cloud->points[Idx3[i]].z << endl; }}

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