1. 直通滤波

代码：

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



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

    // 生成数据
    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 = 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);
    }

    // 打印滤波前数据
    std::cerr << "PointCloud before filtering has: " << 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，对z轴设置范围0.0～1.0之间，不在该范围内的点过滤
    pcl::PassThrough<pcl::PointXYZ> pass;
    pass.setInputCloud(cloud);
    pass.setFilterFieldName("z");
    pass.setFilterLimits(0.0, 1.0);
    pass.filter(*cloud_filtered);

    // 打印滤波后数据
    std::cerr << "PointCloud after filtering has: " << 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;
}

可以对坐标轴进行过滤，

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// Fill in the cloud data
pcl::PCDReader reader;
reader.read("16line.pcd", *cloud);

std::cerr << "Cloud before filtering: " << cloud->points.size() << std::endl;

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered2(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered3(new pcl::PointCloud<pcl::PointXYZ>);

// Create the filtering object
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud(cloud);
pass.setFilterFieldName("x");
pass.setFilterLimits(-5.0, 5.0);
// pass.setFilterLimitsNegative(true);
pass.filter(*cloud_filtered2);
// filter range Y-axis
pass.setInputCloud(cloud_filtered2);
pass.setFilterFieldName("y");
pass.setFilterLimits(-5.0, 5.0);
pass.filter(*cloud_filtered3);
   // filter range Z-axis
pass.setInputCloud(cloud_filtered3);
pass.setFilterFieldName("z");
pass.setFilterLimits(-0.5, 3.0);
pass.filter(*cloud_filtered);

2. VoxelGrid体素网格法下采样

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#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>


int main(){
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);

    // 点云对象读取
    pcl::PCDReader reader;
    reader.read("../table_scene_lms400.pcd", *cloud);

    std::cerr << "PointCloud before filtering has: " << cloud->width * cloud->height
              << " data points.(" <<pcl::getFieldsList(*cloud) << ")." << std::endl;

    // 创建体素网格滤波器对象
    pcl::VoxelGrid<pcl::PointXYZ> vg;
    vg.setInputCloud(cloud); // 设置输入点云
    vg.setLeafSize(0.01f, 0.01f, 0.01f); // 设置体素网格大小
    vg.filter(*cloud_filtered); // 执行滤波操作

    // 输出滤波后的点云信息
    std::cerr << "PointCloud after filtering has: " << cloud_filtered->width * cloud_filtered->height
              << " data points.(" << pcl::getFieldsList(*cloud_filtered) << ")." << std::endl;

    return 0; 
}