一些点云的小知识，从官方文档中发现的例子

1、判断点云的点是否是有效的

pcl::PointXYZ p_valid;
p_valid.x = 0;
p_valid.y = 0;
p_valid.z = 0;
std::cout << "Is p_valid valid? " << pcl::isFinite(p_valid) << std::endl;
 
// If any component is NaN, the point is not finite.
pcl::PointXYZ p_invalid;
p_invalid.x = std::numeric_limits<float>::quiet_NaN();
p_invalid.y = 0;
p_invalid.z = 0;
std::cout << "Is p_invalid valid? " << pcl::isFinite(p_invalid) << std::endl;

　　打印结果：

2、复制同类的点云

// 拷贝点云
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>());
 
pcl::PointCloud<pcl::PointXYZ>::PointType p;// 相当于 pcl::PointXYZ p;
p.x = 1;
p.y = 2;
p.z = 3;
cloud->push_back(p);
std::cout << p.x << " " << p.y << " " << p.z << std::endl;
 
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud2(new pcl::PointCloud<pcl::PointXYZ>());
copyPointCloud(*cloud, *cloud2);// 相同类型复制
 
pcl::PointCloud<pcl::PointXYZ>::PointType p_retrieved = (*cloud2)[0];
//pcl::PointXYZ p_retrieved = cloud2->points.at(0);// 同上
std::cout << p_retrieved.x << " " << p_retrieved.y << " " << p_retrieved.z << std::endl;

　　结果：

3、类型不同的点云复制

// 拷贝点云
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>());
    pcl::PointCloud<pcl::PointXYZ>::PointType p;// 相当于 pcl::PointXYZ p;
    p.x = 1;
    p.y = 2;
    p.z = 3;
    cloud->push_back(p);
    std::cout << p.x << " " << p.y << " " << p.z << std::endl;
 
    pcl::PointCloud<pcl::PointNormal>::Ptr cloud2(new pcl::PointCloud<pcl::PointNormal>());
    copyPointCloud(*cloud, *cloud2);// 不同类型复制，注意cloud2是包含点和法线的，若类型是pcl::Normal会报错
 
    //pcl::PointCloud<pcl::PointNormal>::PointType p_retrieved = (*cloud2)[0];
    pcl::PointNormal p_retrieved = cloud2->points.at(0);// 同上
    std::cout << p_retrieved.x << " " << p_retrieved.y << " " << p_retrieved.z << std::endl;
    std::cout << cloud2->points.at(0).x << " " << cloud2->points.at(0).y << " " << cloud2->points.at(0).z << std::endl;
    std::cout << cloud2->points.at(0).normal[0] << std::endl;
    std::cout << cloud2->points.at(0).normal_y << std::endl;
    std::cout << cloud2->points.at(0).normal[2] << std::endl;

　　结果：

4、获取导入点云文件的最大值和最小值点

pcl::PointXYZ minPt, maxPt;
pcl::getMinMax3D(*n.cloud, minPt, maxPt);
std::cout << "Max x: " << maxPt.x << std::endl;
std::cout << "Max y: " << maxPt.y << std::endl;
std::cout << "Max z: " << maxPt.z << std::endl;
std::cout << "Min x: " << minPt.x << std::endl;
std::cout << "Min y: " << minPt.y << std::endl;
std::cout << "Min z: " << minPt.z << std::endl;    

　　结果：

另一种写法：

// 遍历点云区间
    for (const auto& p : n.cloud->points)
    {
        if (minX > p.x) minX = p.x;
        if (minX > p.y) minY = p.y;
        if (minX > p.z) minZ = p.z;
 
        if (maxX < p.x) maxX = p.x;
        if (maxY < p.y) maxY = p.y;
        if (maxZ < p.z) maxZ = p.z;
    }
    qDebug() << minX << minY << minZ << maxX << maxY << maxZ;

　　结果：

导入的是同一个文件，但为什么结果不一样呢，因为这个没有做无效点判断，而上面那个底层是有判断的

5、组织有序的点云

// Setup the cloud
using PointType = pcl::PointXYZ;
using CloudType = pcl::PointCloud<PointType>;
CloudType::Ptr cloud(new CloudType);
 
// Make the cloud a 10x10 grid
cloud->height = 10;
cloud->width = 10;
cloud->is_dense = true;
cloud->resize(cloud->height * cloud->width);
 
// Output the (0,0) point
std::cout << (*cloud)(0, 0) << std::endl;
 
// Set the (0,0) point
PointType p; p.x = 1; p.y = 2; p.z = 3;
(*cloud)(0, 0) = p;
 
// Confirm that the point was set
std::cout << (*cloud)(0, 0) << std::endl;