• 这是一个demo，用来解释直通滤波
• 首先加载必要的头文件

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/filters/passthrough.h> //直通滤波
#include <pcl/visualization/pcl_visualizer.h>

• 实例化点云对象

	//定义输入和输出点云
	//输入的点云
	pcl::PointCloud<pcl::PointXYZ>::Ptr point_cloud_in_ptr(new 	pcl::PointCloud<pcl::PointXYZ>);
	//滤波后的点云
	pcl::PointCloud<PointT>::Ptr cloud_filtered(new pcl::PointCloud<PointT>);

• 随机生成一个点云

	//创建随机点云
	point_cloud_in_ptr->width = 100000;
	point_cloud_in_ptr->height = 1;
	point_cloud_in_ptr->is_dense = false;
	point_cloud_in_ptr->resize(point_cloud_in_ptr->width * point_cloud_in_ptr->height);
	float a = -1,b = 1;
	for (size_t i = 0; i < point_cloud_in_ptr->points.size(); ++i)
	{
		point_cloud_in_ptr->points[i].x = rand() * 1.0 / RAND_MAX * (b - a) + a;
		point_cloud_in_ptr->points[i].y = rand() * 1.0 / RAND_MAX * (b - a) + a;
		point_cloud_in_ptr->points[i].z = rand() * 1.0 / RAND_MAX * (b - a) + a;
	}

• 这个点云是在区间 x y z 在[-1 ，1]的正方形无序点云
  
  
• 将 Z 轴（蓝色） 在 [-0.5 ，0.5] 区间内的数据过滤

	//直通滤波对象
	pcl::PassThrough<PointT> pass;
	//设置直通滤波
	pass.setInputCloud(point_cloud_in_ptr);
	//设置处理字段是z轴
	pass.setFilterFieldName("z");
	//处理范围区间
	pass.setFilterLimits(-0.5, 0.5);
	//设置是过滤区间内还是区间外 true = 内 false = 外
	pass.setFilterLimitsNegative(true);
	pass.filter(*cloud_filtered);

• 全部代码

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/filters/passthrough.h>
#include <pcl/visualization/pcl_visualizer.h>
/*
* 直通滤波测试
*/

typedef pcl::PointXYZ PointT;

int main() {
	//定义输入和输出点云
	//输入的点云
	pcl::PointCloud<pcl::PointXYZ>::Ptr point_cloud_in_ptr(new pcl::PointCloud<pcl::PointXYZ>);
	//滤波后的点云
	pcl::PointCloud<PointT>::Ptr cloud_filtered(new pcl::PointCloud<PointT>);
	
	//创建随机点云
	point_cloud_in_ptr->width = 100000;
	point_cloud_in_ptr->height = 1;
	point_cloud_in_ptr->is_dense = false;
	point_cloud_in_ptr->resize(point_cloud_in_ptr->width * point_cloud_in_ptr->height);

	float a = -1,b = 1;
	for (size_t i = 0; i < point_cloud_in_ptr->points.size(); ++i)
	{
		point_cloud_in_ptr->points[i].x = rand() * 1.0 / RAND_MAX * (b - a) + a;
		point_cloud_in_ptr->points[i].y = rand() * 1.0 / RAND_MAX * (b - a) + a;
		point_cloud_in_ptr->points[i].z = rand() * 1.0 / RAND_MAX * (b - a) + a;
	}

	//直通滤波对象
	pcl::PassThrough<PointT> pass;
	//设置直通滤波
	pass.setInputCloud(point_cloud_in_ptr);
	//设置处理字段是z轴
	pass.setFilterFieldName("z");
	//处理范围区间
	pass.setFilterLimits(-0.5, 0.5);
	//设置是过滤区间内还是区间外
	pass.setFilterLimitsNegative(true);
	pass.filter(*cloud_filtered);
	std::cerr << "原始点云经过直通滤波处理后的大小 = " << cloud_filtered->size() << std::endl;


	pcl::visualization::PCLVisualizer viewer("v1");
	viewer.addPointCloud(cloud_filtered);
	viewer.addCoordinateSystem();
	viewer.spin();

	return 0;
}