PCL库学习笔记（一）

一、基础内容

1、数据类型

pcl::PointCloud<pcl::PointCloud>

是一个基本的数据类型，PointCloud是C++里的一个类，包含以下函数：

pcl::PointCloud<pcl::PointCloud::width>
pcl::PointCloud<pcl::PointCloud::height>

width:在无序数据集中表示点的总数；有序数据集中表示一行中点的个数。
height：无序数据集为常量1,；有序数据集中表示总行数。

//有序点云
cloud.width = 640; // there are 640 points per line
cloud.height = 480; // thus 640*480=307200 points total in the dataset
//无序点云
cloud.width = 307200;
cloud.height = 1; // unorganized point cloud dataset with 307200 points

若需要判断点云是否有序，无需判断height=1，只需用如下函数

if (!cloud.isOrganized ())

pcl::points<pcl::PointCloud::points>

points:用于存储PointT类型的向量，例如PointXYZ类型的点

pcl::PointCloud<pcl::PointXYZ> cloud;//PointXYZ类型的点储存到点云cloud
std::vector<pcl::PointXYZ> data = cloud.points;//cloud里的数据是储存在points里面的

pcl::is_dense<pcl::PointCloud::is_dense>

bool类型，为真时表示所有点有限，为假时表示有些点可能无限或为空。

2、点的类型

PointXYZ成员变量：float x,y,z;
1.定义一个点云（指针类型）

pcl::PointCloud<pcl::PointXYZ> cloud;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_ptr(new pcl::PointCloud<pcl::PointXYZ>);//指针类型的

2.访问点的坐标

cloud->points[i].x
pcl::PointXYZ point;//创建一个点放到点云里去
point.x = 2.0f - y;
point.y = y;
point.z = z;
cloud.points.push_back(point);

其中 points是个vector变量，points[i]是单个的点、points[i].data[0]、points[i].x用于访问点的x坐标。

二、点云的基本操作

1、写入点云数据

#include 
#include 
#include 

int main(int argc, char** argv)
{
    pcl::PointCloud<pcl::PointXYZ> cloud;

    // 写入点云数据
    cloud.width = 5;
    cloud.height = 1;
    cloud.is_dense = false;
    cloud.points.resize(cloud.width * cloud.height);

    for (std::size_t i = 0; i < cloud.points.size(); ++i)
    {
        cloud.points[i].x = 1024 * rand() / (RAND_MAX + 1.0f);
        cloud.points[i].y = 1024 * rand() / (RAND_MAX + 1.0f);
        cloud.points[i].z = 1024 * rand() / (RAND_MAX + 1.0f);
    }

    pcl::io::savePCDFileASCII("test_pcd.pcd", cloud);//储存在pcd文件中
    std::cerr << "Saved " << cloud.points.size() << " data points to test_pcd.pcd." << std::endl;

    for (std::size_t i = 0; i < cloud.points.size(); ++i)
        std::cerr << "    " << cloud.points[i].x << " " << cloud.points[i].y << " " << cloud.points[i].z << std::endl;

    return (0);
}

2、读取点云数据

#include 
#include 
#include 

int main(int argc, char** argv)
{
    //创建一个PointCloud  Boost共享指针并对其进行初始化。
    //从堆中分配了一个PointCloud对象，把这个对象的指针赋值给了cloud
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);

    if (pcl::io::loadPCDFile<pcl::PointXYZ>("test_pcd.pcd", *cloud) == -1) //* load the file
    {
        PCL_ERROR("Couldn't read file test_pcd.pcd \n");
        return (-1);
    }

    //Loaded 个数 data points from 文件 with the following fields: x y z
    std::cout << "Loaded "
        << cloud->width * cloud->height   //点的个数
        << " data points from test_pcd.pcd with the following fields: "
        << std::endl;

    for (std::size_t i = 0; i < cloud->points.size(); ++i)
        std::cout << "' " << cloud->points[i].x //点云里点的值
        << " " << cloud->points[i].y
        << " " << cloud->points[i].z << std::endl;

    return (0);
}

3、合并点云数据

#include 
#include 
#include 

int main(int argc, char** argv)
{

	pcl::PointCloud<pcl::PointXYZ> cloud_a, cloud_b, cloud_c;//三个输入点云
	pcl::PointCloud<pcl::Normal> n_cloud_b;//两个输出点云，储存法线的点云
	pcl::PointCloud<pcl::PointNormal> p_n_cloud_c;//储存xyz和法线的点云

	// 为两个点云写入数据，points cloud_a 和cloud_b，或者cloud_a 和n_cloud_b
	cloud_a.width = 5;//点个数
	cloud_a.height = cloud_b.height = n_cloud_b.height = 1;//都为无序点云
	cloud_a.points.resize(cloud_a.width * cloud_a.height);

	cloud_b.width = 3;
	cloud_b.points.resize(cloud_b.width * cloud_b.height);

	n_cloud_b.width = 5;//创建n_cloud_b
	n_cloud_b.points.resize(n_cloud_b.width * n_cloud_b.height);

	//写入cloud_a的点
	for (size_t i = 0; i < cloud_a.points.size(); ++i)
	{
		cloud_a.points[i].x = 1024 * rand() / (RAND_MAX + 1.0f);
		cloud_a.points[i].y = 1024 * rand() / (RAND_MAX + 1.0f);
		cloud_a.points[i].z = 1024 * rand() / (RAND_MAX + 1.0f);
	}

	//写入cloud_b的点
	for (size_t i = 0; i < cloud_b.points.size(); ++i)
	{
		cloud_b.points[i].x = 1024 * rand() / (RAND_MAX + 1.0f);
		cloud_b.points[i].y = 1024 * rand() / (RAND_MAX + 1.0f);
		cloud_b.points[i].z = 1024 * rand() / (RAND_MAX + 1.0f);
	}
    //写入n_cloud_b的点
	for (std::size_t i = 0; i < n_cloud_b.points.size(); ++i)
	{
		n_cloud_b.points[i].normal[0] = 1024 * rand() / (RAND_MAX + 1.0f);
		n_cloud_b.points[i].normal[1] = 1024 * rand() / (RAND_MAX + 1.0f);
		n_cloud_b.points[i].normal[2] = 1024 * rand() / (RAND_MAX + 1.0f);
	}
	
	//输出刚刚写入点云的点的数据
	std::cerr << "Cloud A: " << std::endl;
	for (size_t i = 0; i < cloud_a.points.size(); ++i)
		std::cerr << "    " << cloud_a.points[i].x << " " << cloud_a.points[i].y << " " << cloud_a.points[i].z << std::endl;

	std::cerr << "Cloud B: " << std::endl;
	for (size_t i = 0; i < cloud_b.points.size(); ++i)
		std::cerr << "    " << cloud_b.points[i].x << " " << cloud_b.points[i].y << " " << cloud_b.points[i].z << std::endl;

	std::cerr << "n_Cloud B: " << std::endl;
	for (std::size_t i = 0; i < n_cloud_b.points.size(); ++i)//输出法线点的方式不一样
		std::cerr << "    " << n_cloud_b.points[i].normal[0] << " " << n_cloud_b.points[i].normal[1] << " " << n_cloud_b.points[i].normal[2] << std::endl;
	

	//连接点云到c
	cloud_c = cloud_a;
	cloud_c += cloud_b;
	std::cerr << "Cloud C: " << std::endl;
	for (size_t i = 0; i < cloud_c.points.size(); ++i)
		std::cerr << "    " << cloud_c.points[i].x << " " << cloud_c.points[i].y << " " << cloud_c.points[i].z << " " << std::endl;

	//连接字段到p_n_cloud_c
	pcl::concatenateFields(cloud_a, n_cloud_b, p_n_cloud_c);//连接字段到p_n_cloud_c
	std::cerr << "Cloud D: " << std::endl;
	for (size_t i = 0; i < p_n_cloud_c.points.size(); ++i)
		std::cerr << "    " <<
		p_n_cloud_c.points[i].x << " " << p_n_cloud_c.points[i].y << " " << p_n_cloud_c.points[i].z << " " <<
		p_n_cloud_c.points[i].normal[0] << " " << p_n_cloud_c.points[i].normal[1] << " " << p_n_cloud_c.points[i].normal[2] << std::endl;

	return (0);
}

以上介绍了两种不同的连接方式，每一种都有自己的约束条件，代码的输出结果为

Cloud A:
    1.28125 577.094 197.938
    828.125 599.031 491.375
    358.688 917.438 842.562
    764.5 178.281 879.531
    727.531 525.844 311.281
Cloud B:
    15.3438 93.5938 373.188
    150.844 169.875 1012.22
    456.375 121.938 4.78125
n_Cloud B:
    9.125 386.938 544.406
    584.875 616.188 621.719
    170.219 678.938 461.594
    360.562 58.4062 622.25
    802.094 821.844 532.344
Cloud C:
    1.28125 577.094 197.938
    828.125 599.031 491.375
    358.688 917.438 842.562
    764.5 178.281 879.531
    727.531 525.844 311.281
    15.3438 93.5938 373.188
    150.844 169.875 1012.22
    456.375 121.938 4.78125
Cloud D:
    1.28125 577.094 197.938 9.125 386.938 544.406
    828.125 599.031 491.375 584.875 616.188 621.719
    358.688 917.438 842.562 170.219 678.938 461.594
    764.5 178.281 879.531 360.562 58.4062 622.25
    727.531 525.844 311.281 802.094 821.844 532.344