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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#xff0c;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#xff0c;或者k维的树是一个计算机科学里面的数据…from:https://blog.csdn.net/qq_25491201/article/details/51135054
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::PointCloudpcl::PointXYZ::Ptr cloud (new pcl::PointCloudpcl::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::KdTreeFLANNpcl::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::vectorint pointIdxNKNSearch(K); std::vectorfloat 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::vectorint pointIdxRadiusSearch; std::vectorfloat 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::KdTreeFLANNpcl::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::vectorint pointIdxNKNSearch(K); std::vectorfloat 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::vectorint pointIdxRadiusSearch; std::vectorfloat 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 K10 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 radius225.932 494.728 371.875 351.687 (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) --------------------- 作者Spongelady 来源CSDN 原文https://blog.csdn.net/qq_25491201/article/details/51135054 版权声明本文为博主原创文章转载请附上博文链接
