PCL（20）直通滤波

#include <iostream>
#include <ctime>
#include <pcl/point_types.h>
#include <pcl/filters/passthrough.h>
int
main(int argc, char** argv)
{
	srand(time(0));
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);
	//填入点云数据
	cloud->width = 5;
	cloud->height = 1;
	cloud->points.resize(cloud->width * cloud->height);
	for (size_t i = 0; i < cloud->points.size(); ++i)
	{
		cloud->points[i].x = rand() / (RAND_MAX + 1.0f) - 0.5;
		cloud->points[i].y = rand() / (RAND_MAX + 1.0f) - 0.5;
		cloud->points[i].z = rand() / (RAND_MAX + 1.0f) - 0.5;
	}
	std::cerr << "Cloud before filtering: " << std::endl;
	for (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;
	// 创建滤波器对象
	pcl::PassThrough<pcl::PointXYZ> pass;//创建对象
	pass.setInputCloud(cloud);//输入待处理点云
	pass.setFilterFieldName("z");//输入过滤段的名字
	pass.setFilterLimits(0.0, 1.0);//设置点云保留区间
	//pass.setFilterLimitsNegative (true);
	pass.filter(*cloud_filtered);//生成过滤后的点云

	std::cerr << "Cloud after filtering: " << std::endl;
	for (size_t i = 0; i < cloud_filtered->points.size(); ++i)
		std::cerr << "    " << cloud_filtered->points[i].x << " "
		<< cloud_filtered->points[i].y << " "
		<< cloud_filtered->points[i].z << std::endl;
	return (0);
}

标签：20,pcl,points,PCL,pass,1.0,size,cloud,通滤波
来源： https://blog.csdn.net/kangjielearning/article/details/112134957