tricp点云配准

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <iostream>
#include <pcl/recognition/trimmed_icp.h>//tricp头文件
#include <time.h>
#include <boost/thread/thread.hpp>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/filter.h>


using namespace std;
typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloud;


void visualize_pcd(PointCloud::Ptr pcd_src, PointCloud::Ptr pcd_tgt, PointCloud::Ptr pcd_final)
{
    pcl::visualization::PCLVisualizer viewer("registration Viewer");
    //原始点云绿色
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> src_h(pcd_src, 0, 255, 0);
    //目标点云红色
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> tgt_h(pcd_tgt, 255, 0, 0);
    //匹配好的点云蓝色
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> final_h(pcd_final, 0, 0, 255);

    viewer.setBackgroundColor(255, 255, 255);
    viewer.addPointCloud(pcd_src, src_h, "source cloud");
    viewer.addPointCloud(pcd_tgt, tgt_h, "target cloud");
    viewer.addPointCloud(pcd_final, final_h, "result cloud");
    while (!viewer.wasStopped())
    {
        viewer.spinOnce(100);
        boost::this_thread::sleep(boost::posix_time::microseconds(100000));
    }

}

int main(int argc, char** argv)
{
    //加载点云文件
    PointCloud::Ptr cloud_source(new PointCloud);
    PointCloud::Ptr cloud_target(new PointCloud);

    //std::string filename = "point_source/hippo1-1224_s100.pcd";//
    std::string filename = "target.pcd";//

    if (pcl::io::loadPCDFile<pcl::PointXYZ>(filename, *cloud_target) == -1)//*打开点云文件

    {
        PCL_ERROR("Couldn't read file test_pcd.pcd\n");
        return(-1);
    }

    //filename = "point_source/hippo2-1224_s100.pcd";//
    filename = "source.pcd";//

    if (pcl::io::loadPCDFile<pcl::PointXYZ>(filename, *cloud_source) == -1)//*打开点云文件

    {
        PCL_ERROR("Couldn't read file test_pcd.pcd\n");
        return(-1);
    }
    pcl::PointCloud<pcl::PointXYZ>::Ptr filtered_cloud(new pcl::PointCloud<pcl::PointXYZ>);//定义降采样后点云
    *filtered_cloud = *cloud_source;

    //体素降采样
    pcl::VoxelGrid<pcl::PointXYZ> voxel_grid;
    voxel_grid.setLeafSize(0.5, 0.5, 0.5);//bunny
    //voxel_grid.setLeafSize(2.0, 2.0, 2.0);//hippo
    voxel_grid.setInputCloud(filtered_cloud);
    voxel_grid.filter(*filtered_cloud);
    std::cout << "down size input_cloud to" << filtered_cloud->size() << endl;//显示降采样后的点云数量

    pcl::recognition::TrimmedICP<PointT, double> tricp;
    tricp.init(cloud_target);//target

    Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity();
    clock_t start = clock();
    /*align(source, num_source_points_to_use, guess_and_result)
    * source是输入待配准点云（源点云）
    * num_source_points_to_use就是点云乘以重叠度，不同点云模型重叠度
    * guess_and_result是输出算法得到的变换矩阵
    */
    tricp.align(*filtered_cloud, (int)filtered_cloud->size() * 0.8f, transformation_matrix);
    pcl::io::savePCDFileBinary("tricp.pcd", *filtered_cloud);
    clock_t end = clock();

    cout << "time:" << (double)(end - start) / (double)CLOCKS_PER_SEC << " s" << endl;
    cout << "matrix:" << endl << transformation_matrix << endl << endl << endl;

    PointCloud::Ptr cloud_end(new PointCloud);
    pcl::transformPointCloud(*cloud_source, *cloud_end, transformation_matrix);

    visualize_pcd(cloud_source, cloud_target, cloud_end);
    return (0);


}