代码目的：
假设有两片点云cloudA、cloudB，若在cloudB中找到cloudA的数据点，则从cloudB中删除该点。

#include <pcl/point_cloud.h>        //点类型定义头文件
#include <pcl/kdtree/kdtree_flann.h> //kdtree类定义头文件

#include <iostream>
#include <vector>
#include <ctime>
const double eps = 1.0e-6;

int
main(int argc, char** argv)
{
    //srand(time(NULL));   //用系统时间初始化随机种子
    //创建一个PointCloud<pcl::PointXYZ>
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloudA(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloudB(new pcl::PointCloud<pcl::PointXYZ>);
    // 随机点云生成
    cloudA->width = 20;             //此处点云数量
    cloudA->height = 1;                //表示点云为无序点云
    cloudA->points.resize(cloudA->width * cloudA->height);

    for (size_t i = 0; i < cloudA->points.size(); ++i)   //循环填充点云数据
    {
        cloudA->points[i].x = i + 1;
        cloudA->points[i].y = i + 1;
        cloudA->points[i].z = i + 1;
    }

    cloudB->width = 20;             //此处点云数量
    cloudB->height = 1;                //表示点云为无序点云
    cloudB->points.resize(cloudB->width * cloudB->height);
    for (size_t i = 0; i < cloudB->points.size(); ++i)   //循环填充点云数据
    {
        cloudB->points[i].x = i + 8;
        cloudB->points[i].y = i + 8;
        cloudB->points[i].z = i + 8;
    }
    //cloudBackup->points = cloud->points;
    //创建KdTreeFLANN对象，并把创建的点云设置为输入,创建一个searchPoint变量作为查询点
    pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;

    pcl::PointXYZ searchPoint;
    int K = 1;
    std::vector<int> pointIdxNKNSearch(K);      //存储查询点近邻索引
    std::vector<float> pointNKNSquaredDistance(K); //存储近邻点对应距离平方
    std::vector<pcl::PointXYZ> DeleteData;
    int num = 0;
    for (auto iterA = cloudA->begin(); iterA != cloudA->end(); iterA++)
    {
        searchPoint.x = iterA->x;
        searchPoint.y = iterA->y;
        searchPoint.z = iterA->z;
        kdtree.setInputCloud(cloudB);    //在cloudB中找到对应点后，在cloudB中直接删除该点
        num = kdtree.nearestKSearch(searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance);
        if (num > 0)
        {
            if (sqrt(pointNKNSquaredDistance[0])<eps)
            {
                auto iterB = cloudB->begin() + pointIdxNKNSearch[0];
                cloudB->erase(iterB);
                DeleteData.push_back(searchPoint);
                if (cloudB->size()==0)
                {
                    break;
                }
                searchPoint.x = 0;
                searchPoint.y = 0;
                searchPoint.z = 0;
                num = 0;
                pointIdxNKNSearch.clear();
                pointNKNSquaredDistance.clear();
            }
        }
        
    }
    
    for (auto iter = DeleteData.begin(); iter != DeleteData.end(); iter++)
    {
        std::cout << iter->x << " " << iter->y << " " << iter->z << std::endl;
    }

    system("pause");
    return 0;
}