工程代码：简单地测试了k-d树的最近邻搜索功能

#include<pcl/point_cloud.h>
#include<pcl/kdtree/kdtree_flann.h>
 
#include<iostream>
#include<vector>
#include<ctime>
 
using namespace std;
 
int
main(int argc, char** argv)
{
	//使用系统时间做随机数种子
	srand(time(NULL));
	//创建一个PointXYZ类型点云指针
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
 
	//初始化点云数据
	cloud->width = 1000;//宽为1000
	cloud->height = 1;//高为1，说明为无序点云
	cloud->points.resize(cloud->width * cloud->height);
	//使用随机数填充数据
 
	for (size_t i = 0; i < cloud->size(); ++i)
	{
		//PointCloud类中对[]操作符进行了重载，返回的是对points的引用
		// (*cloud)[i].x 等同于 cloud->points[i].x
		(*cloud)[i].x = 1024.0f * rand() / (RAND_MAX + 1.0f);
		cloud->points[i].y = 1024.0f * rand() / (RAND_MAX + 1.0f);//推进写法
		cloud->points[i].z = 1024.0f * rand() / (RAND_MAX + 1.0f);//推进写法
	}
 
	//创建kd树对象
	pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;
 
	//设置点云输入,将在cloud中搜索
	kdtree.setInputCloud(cloud);
 
	//设置被搜索点,用随机数填充
	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);
 
	//开始k最近邻搜索
	int K = 10;
	vector<int> pointIdxNKNSearch(K);
	vector<float> pointNKNSquaredDistance(K);
 
	cout << "K nearest neighbor search at (" << searchPoint.x
		<< " " << searchPoint.y
		<< " " << searchPoint.z
		<< ") with K = " << K << endl;
 
	if (kdtree.nearestKSearch(searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)
	{
		for (size_t i = 0; i < pointIdxNKNSearch.size(); ++i)
		{
			cout << "    " << cloud->points[pointIdxNKNSearch[i]].x
				<< " " << cloud->points[pointIdxNKNSearch[i]].x
				<< " " << cloud->points[pointIdxNKNSearch[i]].z
				<< "( squared distance: " << pointNKNSquaredDistance[i] << " )";
		}
	}
 
	//基于半径的邻域搜索
	//搜索结果保存在两个数组中，一个是下标，一个是距离
	vector<int> pointIdxRadiusSearch;
	vector<float> pointRadiusSquaredDistance;
 
	//设置搜索半径，随机值
	float radius = 256.0f* rand() / (RAND_MAX + 1.0f);
 
	cout << "Neighbors within radius search at (" << searchPoint.x
		<< " " << searchPoint.y
		<< " " << searchPoint.z
		<< ") with radius=" << radius << endl;
 
	if (kdtree.radiusSearch(searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0)
	{
		for (size_t i = 0; i < pointIdxRadiusSearch.size(); ++i)
		{
			cout << "    " << cloud->points[pointIdxRadiusSearch[i]].x
				<< " " << cloud->points[pointIdxRadiusSearch[i]].x
				<< " " << cloud->points[pointIdxRadiusSearch[i]].z
				<< "( squared distance: " << pointRadiusSquaredDistance[i] << " )";
		}
	}
	return 0;
}

但出现了如下错误：

error C3861: “pop_t”: 找不到标识符

 解决办法

打开pcl 1.8.1\3rdparty\flann\include\flann\algorithms\dist.h文件：

错误定位在了523行：

result = lut(reinterpret_cast<const unsigned char*> (a),
                     reinterpret_cast<const unsigned char*> (b), size * sizeof(pop_t));

原来是在当前作用域中找不到pop_t数据类型的定义，从上文代码中，将相关的定义复制到当前作用域中即可：

typedef unsigned long long pop_t;

 

重新编译，问题解决。