PCL高斯滤波的示例代码

以下是PCL高斯滤波的示例代码：

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/filters/gaussian.h>

int main()
{
    // 创建点云
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    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 = 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);
    }

    // 创建高斯滤波器对象
    pcl::GaussianKernel<pcl::PointXYZ, pcl::PointXYZ>::Ptr kernel(new pcl::GaussianKernel<pcl::PointXYZ, pcl::PointXYZ>);
    pcl::Convolution<pcl::PointXYZ, pcl::PointXYZ, pcl::GaussianKernel<pcl::PointXYZ, pcl::PointXYZ> > convolution;
    convolution.setInputCloud(cloud);
    convolution.setKernel(kernel);

    // 执行高斯滤波
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);
    convolution.convolve(*cloud_filtered);

    // 输出滤波结果
    std::cout << "Input cloud: " << std::endl;
    for (size_t i = 0; i < cloud->points.size(); ++i)
        std::cout << "    " << cloud->points[i].x << " " << cloud->points[i].y << " " << cloud->points[i].z << std::endl;
    std::cout << "Filtered cloud: " << std::endl;
    for (size_t i = 0; i < cloud_filtered->points.size(); ++i)
        std::cout << "    " << cloud_filtered->points[i].x << " " << cloud_filtered->points[i].y << " " << cloud_filtered->points[i].z << std::endl;

    return 0;
}

这段代码创建一个具有随机点的PointCloud对象，然后创建一个高斯滤波器对象并将其应用于PointCloud。最终，输出原始和过滤PointCloud的坐标。请注意，此示例使用的是Convolution类，该类允许将自定义核函数应用于PointCloud。如果您只需要使用标准的高斯核，可以使用pcl::GaussianFilter类。