PCL点云变化检测C++代码

以下是一个简单的 PCL 点云变化检测的 C++ 代码示例：

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/icp.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/visualization/pcl_visualizer.h>

int main(int argc, char** argv)
{
    // 读取第一个点云
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in(new pcl::PointCloud<pcl::PointXYZ>);
    if (pcl::io::loadPCDFile<pcl::PointXYZ>("cloud_in.pcd", *cloud_in) == -1)
    {
        PCL_ERROR("Couldn't read file cloud_in.pcd! \n");
        return (-1);
    }
    std::cout << "Loaded " << cloud_in->size() << " data points from cloud_in.pcd" << std::endl;

    // 读取第二个点云
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out(new pcl::PointCloud<pcl::PointXYZ>);
    if (pcl::io::loadPCDFile<pcl::PointXYZ>("cloud_out.pcd", *cloud_out) == -1)
    {
        PCL_ERROR("Couldn't read file cloud_out.pcd! \n");
        return (-1);
    }
    std::cout << "Loaded " << cloud_out->size() << " data points from cloud_out.pcd" << std::endl;

    // 下采样
    pcl::VoxelGrid<pcl::PointXYZ> sor;
    sor.setInputCloud(cloud_in);
    sor.setLeafSize(0.01f, 0.01f, 0.01f);
    sor.filter(*cloud_in);
    std::cout << "cloud_in after filtering: " << cloud_in->size() << std::endl;

    sor.setInputCloud(cloud_out);
    sor.filter(*cloud_out);
    std::cout << "cloud_out after filtering: " << cloud_out->size() << std::endl;

    // ICP 配准
    pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
    icp.setInputSource(cloud_in);
    icp.setInputTarget(cloud_out);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_icp(new pcl::PointCloud<pcl::PointXYZ>);
    icp.align(*cloud_icp);

    std::cout << "ICP has converged with score: " << icp.getFitnessScore() << std::endl;

    // 可视化
    pcl::visualization::PCLVisualizer viewer("ICP demo");
    int v1(0), v2(0);
    viewer.createViewPort(0.0, 0.0, 0.5, 1.0, v1);
    viewer.createViewPort(0.5, 0.0, 1.0, 1.0, v2);
    viewer.setBackgroundColor(0, 0, 0, v1);
    viewer.setBackgroundColor(0.05, 0.05, 0.05, v2);
    viewer.addPointCloud(cloud_in, "cloud_in", v1);
    viewer.addPointCloud(cloud_out, "cloud_out", v2);
    viewer.addPointCloud(cloud_icp, "cloud_icp", v2);
    viewer.spin();

    return 0;
}

该代码使用 PCL 库实现点云的下采样和 ICP 配准，并使用 PCL 可视化工具可视化结果。请注意，此示例仅针对简单的场景，实际应用中需要根据具体情况进行调整。