Search-Octree-PCL-Cpp (70%)
PCL-CPP 기반 Octree 탐색
코드는 [이곳]에서 다운로드 가능합니다. 샘플파일은 [cloud_cluster_0.pcd]을 사용하였습니다.
#include <pcl/io/pcd_io.h>
#include <pcl/octree/octree_search.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/point_types.h>
#include <iostream>
#include <vector>
//Spatial Partitioning and Search Operations with Octrees
//http://pointclouds.org/documentation/tutorials/octree.php#octree-search
//Commnets : Hunjung, Lim (hunjung.lim@hotmail.com)
int main()
{
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>);
// *.PCD 파일 읽기 (https://raw.githubusercontent.com/adioshun/gitBook_Tutorial_PCL/master/Intermediate/sample/cloud_cluster_0.pcd)
pcl::io::loadPCDFile<pcl::PointXYZRGB>("cloud_cluster_0.pcd", *cloud);
// 시각적 확인을 위해 색상 통일 (255,255,255)
for (size_t i = 0; i < cloud->points.size(); ++i){
cloud->points[i].r = 255;
cloud->points[i].g = 255;
cloud->points[i].b = 255;
}
//Octree 오브젝트 생성
float resolution = 0.03f; //복셀 크기 설정(Set octree voxel resolution)
pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree(resolution);
octree.setInputCloud(cloud); // 입력
octree.addPointsFromInputCloud(); //Octree 생성 (Build Octree)
//기준점(searchPoint) 설정 방법 #1(x,y,z 좌표 지정)
//pcl::PointXYZRGB searchPoint;
//searchPoint.x = 0.026256f;
//searchPoint.y = -1.464739f;
//searchPoint.z = 0.929567f;
//기준점(searchPoint) 설정 방법 #2(3000번째 포인트)
pcl::PointXYZRGB searchPoint = cloud->points[3000];
//기준점 좌표 출력
std::cout << "searchPoint :" << searchPoint.x << " " << searchPoint.y << " " << searchPoint.z << std::endl;
//기준점과 동일한 복셀내 존재 하는 하는 포인트 탐색(Voxel Neighbor Search)
std::vector<int> pointIdxVec; //결과물 포인트의 Index 저장(Save the result vector of the voxel neighbor search)
if (octree.voxelSearch(searchPoint, pointIdxVec))
{
//시각적 확인을 위하여 색상 변경 (255,0,0)
for (size_t i = 0; i < pointIdxVec.size(); ++i){
cloud->points[pointIdxVec[i]].r = 255;
cloud->points[pointIdxVec[i]].g = 0;
cloud->points[pointIdxVec[i]].b = 0;
}
}
// 기준점에서 가까운 순서중 K번째까지의 포인트 탐색 (K nearest neighbor search)
int K = 50; // 탐색할 포인트 수 설정
std::vector<int> pointIdxNKNSearch; //Save the index result of the K nearest neighbor
std::vector<float> pointNKNSquaredDistance; //Save the index result of the K nearest neighbor
if (octree.nearestKSearch(searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)
{
//시각적 확인을 위하여 색상 변경 (0,255,0)
for (size_t i = 0; i < pointIdxNKNSearch.size(); ++i){
cloud->points[pointIdxNKNSearch[i]].r = 0;
cloud->points[pointIdxNKNSearch[i]].g = 255;
cloud->points[pointIdxNKNSearch[i]].b = 0;
}
}
// 탐색된 점의 수 출력
std::cout << "K = 50 nearest neighbors:" << pointIdxNKNSearch.size() << endl;
//기준점에서 지정된 반경내 포인트 탐색 (Neighbor search within radius)
float radius = 0.02; //탐색할 반경 설정(Set the search radius)
std::vector<int> pointIdxRadiusSearch; //Save the index of each neighbor
std::vector<float> pointRadiusSquaredDistance; //Save the square of the Euclidean distance between each neighbor and the search point
if (octree.radiusSearch(searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0)
{
//시각적 확인을 위하여 색상 변경 (0,0,255)
for (size_t i = 0; i < pointIdxRadiusSearch.size(); ++i){
cloud->points[pointIdxRadiusSearch[i]].r = 0;
cloud->points[pointIdxRadiusSearch[i]].g = 0;
cloud->points[pointIdxRadiusSearch[i]].b = 255;
}
}
// 탐색된 점의 수 출력
std::cout << "Radius 0.02 nearest neighbors: " << pointIdxRadiusSearch.size() << endl;
// 생성된 포인트클라우드 저장
pcl::io::savePCDFile<pcl::PointXYZRGB>("Octree_AllinOne.pcd", *cloud);
}결과
searchPoint :0.0346006 -1.46636 0.975463
K = 50 nearest neighbors:50
Radius 0.02 nearest neighbors: 141참고위치 | 결과 |
각 기능별 코드 3개
1. Neighbors within voxel search
코드는 [이곳]에서 다운로드 가능합니다. 샘플파일은 [cloud_cluster_0.pcd]을 사용하였습니다.
#include <pcl/point_cloud.h>
#include <pcl/io/pcd_io.h>
#include <pcl/octree/octree_search.h>
#include <iostream>
#include <vector>
#include <ctime>
int
main (int argc, char** argv)
{
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::io::loadPCDFile<pcl::PointXYZRGB>("cloud_cluster_0.pcd", *cloud);
float resolution = 128.0f;
pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree (resolution);
octree.setInputCloud (cloud);
octree.addPointsFromInputCloud ();
pcl::PointXYZRGB searchPoint;
searchPoint.x = 0.026256f;
searchPoint.y = -1.464739f;
searchPoint.z = 0.929567f;
// Neighbors within voxel search
std::vector<int> pointIdxVec;
if (octree.voxelSearch (searchPoint, pointIdxVec))
{
std::cout << "Neighbors within voxel search at (" << searchPoint.x
<< " " << searchPoint.y
<< " " << searchPoint.z << ")"
<< std::endl;
for (size_t i = 0; i < pointIdxVec.size (); ++i)
std::cout << " " << cloud->points[pointIdxVec[i]].x
<< " " << cloud->points[pointIdxVec[i]].y
<< " " << cloud->points[pointIdxVec[i]].z << std::endl;
}
}결과
...
-0.00606756 -1.46653 0.797328
-0.00904433 -1.46755 0.796737
-0.0120327 -1.46887 0.795969
...2. K nearest neighbor search
코드는 [이곳]에서 다운로드 가능합니다. 샘플파일은 [cloud_cluster_0.pcd]을 사용하였습니다.
#include <pcl/point_cloud.h>
#include <pcl/io/pcd_io.h>
#include <pcl/octree/octree_search.h>
#include <iostream>
#include <vector>
#include <ctime>
int
main (int argc, char** argv)
{
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::io::loadPCDFile<pcl::PointXYZRGB>("cloud_cluster_0.pcd", *cloud);
float resolution = 128.0f;
pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree (resolution);
octree.setInputCloud (cloud);
octree.addPointsFromInputCloud ();
pcl::PointXYZRGB searchPoint;
searchPoint.x = 0.026256f;
searchPoint.y = -1.464739f;
searchPoint.z = 0.929567f;
// K nearest neighbor search
int K = 10;
std::vector<int> pointIdxNKNSearch;
std::vector<float> pointNKNSquaredDistance;
std::cout << "K nearest neighbor search at (" << searchPoint.x
<< " " << searchPoint.y
<< " " << searchPoint.z
<< ") with K=" << K << std::endl;
if (octree.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;
}
}결과
K nearest neighbor search at (0.026256 -1.46474 0.929567) with K=10
0.0262559 -1.46474 0.929567 (squared distance: 3.69042e-13)
0.0234182 -1.46435 0.929759 (squared distance: 8.24415e-06)
0.0290953 -1.46517 0.929357 (squared distance: 8.28962e-06)
0.0262519 -1.46476 0.932708 (squared distance: 9.86657e-06)
0.0262599 -1.46472 0.926419 (squared distance: 9.90814e-06)
0.0290885 -1.46518 0.932502 (squared distance: 1.68363e-05)
0.0234196 -1.46433 0.926612 (squared distance: 1.69452e-05)
0.0234169 -1.46437 0.932899 (squared distance: 1.93018e-05)
0.029102 -1.46515 0.926206 (squared distance: 1.95655e-05)
0.0205821 -1.46402 0.929919 (squared distance: 3.28378e-05)3. Neighbors within radius search
코드는 [이곳]에서 다운로드 가능합니다. 샘플파일은 [cloud_cluster_0.pcd]을 사용하였습니다.
#include <pcl/point_cloud.h>
#include <pcl/io/pcd_io.h>
#include <pcl/octree/octree_search.h>
#include <iostream>
#include <vector>
#include <ctime>
int
main (int argc, char** argv)
{
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::io::loadPCDFile<pcl::PointXYZRGB>("cloud_cluster_0.pcd", *cloud);
float resolution = 128.0f;
pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree (resolution);
octree.setInputCloud (cloud);
octree.addPointsFromInputCloud ();
pcl::PointXYZRGB searchPoint;
searchPoint.x = 0.026256f;
searchPoint.y = -1.464739f;
searchPoint.z = 0.929567f;
// 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 (octree.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;
}
}결과
...
-0.00606756 -1.46653 0.797328
-0.00904433 -1.46755 0.796737
-0.0120327 -1.46887 0.795969
...Last updated
