pcl调用icp算法拼接两个点云

丰衣足食靠自己。

PCL:1.8.1
IDE:vs2017
拼接两个角度的点云 main.cpp.

#include <iostream>                 //标准输入输出头文件
#include <pcl/io/pcd_io.h>          //I/O操作头文件
#include <pcl/point_types.h>        //点类型定义头文件
#define BOOST_TYPEOF_EMULATION  //要加在#include 前
#include <pcl/registration/icp.h>   //ICP配准类相关头文件
#include <pcl/visualization/cloud_viewer.h>//点云查看窗口头文件
#include <pcl/visualization/pcl_visualizer.h>//可视化头文件

typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloudT;

bool next_iteration = false;

void print4x4Matrix(const Eigen::Matrix4d & matrix)    //打印旋转矩阵和平移矩阵
{
	printf("Rotation matrix :\n");
	printf("    | %6.3f %6.3f %6.3f | \n", matrix(0, 0), matrix(0, 1), matrix(0, 2));
	printf("R = | %6.3f %6.3f %6.3f | \n", matrix(1, 0), matrix(1, 1), matrix(1, 2));
	printf("    | %6.3f %6.3f %6.3f | \n", matrix(2, 0), matrix(2, 1), matrix(2, 2));
	printf("Translation vector :\n");
	printf("t = < %6.3f, %6.3f, %6.3f >\n\n", matrix(0, 3), matrix(1, 3), matrix(2, 3));
}

void KeyboardEventOccurred(const pcl::visualization::KeyboardEvent& event, void* nothing)
{  //使用空格键来增加迭代次数，并更新显示
	if (event.getKeySym() == "space" && event.keyDown())
		next_iteration = true;
}


int main(int argc, char** argv)
{
	// 创建点云指针
	PointCloudT::Ptr cloud_in(new PointCloudT);  // 原始点云
	PointCloudT::Ptr cloud_icp(new PointCloudT);  // ICP 输出点云
	PointCloudT::Ptr cloud_tr(new PointCloudT);  // 匹配点云

	//读取pcd文件
	if (pcl::io::loadPCDFile<pcl::PointXYZ>("YourPCDFile01.pcd", *cloud_in) == -1)
	{
		PCL_ERROR("Couldn't read file1 \n");
		return (-1);
	}
	std::cout << "Loaded " << cloud_in->size() << " data points from file1" << std::endl;

	if (pcl::io::loadPCDFile<pcl::PointXYZ>("YourPCDFile02.pcd", *cloud_icp) == -1)
	{
		PCL_ERROR("Couldn't read file2 \n");
		return (-1);
	}
	std::cout << "Loaded " << cloud_icp->size() << " data points from file2" << std::endl;

	int iterations = 1;  // 默认的ICP迭代次数
	*cloud_tr = *cloud_icp;
	//icp配准
	pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp; //创建ICP对象，用于ICP配准
	icp.setMaximumIterations(iterations);    //设置最大迭代次数iterations=true
	icp.setInputCloud(cloud_tr); //设置输入点云
	icp.setInputTarget(cloud_in); //设置目标点云（输入点云进行仿射变换，得到目标点云）
	icp.align(*cloud_icp);          //匹配后源点云
	//pcl::io::savePLYFile("Final.ply",*cloud_icp); //保存
	icp.setMaximumIterations(1);  // 设置为1以便下次调用
	std::cout << "Applied " << iterations << " ICP iteration(s)" << std::endl;
	if (icp.hasConverged())//icp.hasConverged ()=1（true）输出变换矩阵的适合性评估
	{
		std::cout << "\nICP has converged, score is " << icp.getFitnessScore() << std::endl;
		std::cout << "\nICP transformation " << iterations << " : cloud_icp -> cloud_in" << std::endl;
	}
	else
	{
		PCL_ERROR("\nICP has not converged.\n");
		return (-1);
	}


	//可视化
	pcl::visualization::PCLVisualizer viewer("ICP demo");
	// 创建两个观察视点
	int v1(0);
	int v2(1);
	viewer.createViewPort(0.0, 0.0, 0.5, 1.0, v1);
	viewer.createViewPort(0.5, 0.0, 1.0, 1.0, v2);
	// 定义显示的颜色信息
	float bckgr_gray_level = 0.0;  // Black
	float txt_gray_lvl = 1.0 - bckgr_gray_level;
	// 原始的点云设置为白色的
	pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_in_color_h(cloud_in, (int)255 * txt_gray_lvl, (int)255 * txt_gray_lvl,
		(int)255 * txt_gray_lvl);

	viewer.addPointCloud(cloud_in, cloud_in_color_h, "cloud_in_v1", v1);//设置原始的点云都是显示为白色
	viewer.addPointCloud(cloud_in, cloud_in_color_h, "cloud_in_v2", v2);

	// 转换后的点云显示为绿色
	pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_tr_color_h(cloud_tr, 20, 180, 20);
	viewer.addPointCloud(cloud_tr, cloud_tr_color_h, "cloud_tr_v1", v1);

	// ICP配准后的点云为红色
	pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_icp_color_h(cloud_icp, 180, 20, 20);
	viewer.addPointCloud(cloud_icp, cloud_icp_color_h, "cloud_icp_v2", v2);

	// 加入文本的描述在各自的视口界面
	//在指定视口viewport=v1添加字符串“white 。。。”其中"icp_info_1"是添加字符串的ID标志，（10，15）为坐标16为字符大小 后面分别是RGB值
	viewer.addText("White: Original point cloud\nGreen: Matrix transformed point cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_1", v1);
	viewer.addText("White: Original point cloud\nRed: ICP aligned point cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_2", v2);

	std::stringstream ss;
	ss << iterations;            //输入的迭代的次数
	std::string iterations_cnt = "ICP iterations = " + ss.str();
	viewer.addText(iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt", v2);

	// 设置背景颜色
	viewer.setBackgroundColor(bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v1);
	viewer.setBackgroundColor(bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v2);

	// 设置相机的坐标和方向
	viewer.setCameraPosition(-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
	viewer.setSize(1280, 1024);  // 可视化窗口的大小

	// 注册按键回调函数
	viewer.registerKeyboardCallback(&KeyboardEventOccurred, (void*)NULL);

	//显示
	while (!viewer.wasStopped())
	{
		viewer.spinOnce();

		//按下空格键的函数
		if (next_iteration)
		{
			// 最近点迭代算法
			icp.align(*cloud_icp);
			if (icp.hasConverged())
			{
				printf("\033[11A");  // Go up 11 lines in terminal output.
				printf("\nICP has converged, score is %+.0e\n", icp.getFitnessScore());
				std::cout << "\nICP transformation " << ++iterations << " : cloud_icp -> cloud_in" << std::endl;

				ss.str("");
				ss << iterations;
				std::string iterations_cnt = "ICP iterations = " + ss.str();
				viewer.updateText(iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt");
				viewer.updatePointCloud(cloud_icp, cloud_icp_color_h, "cloud_icp_v2");
			}
			else
			{
				PCL_ERROR("\nICP has not converged.\n");
				return (-1);
			}
		}
		next_iteration = false;
	}


	return 0;
}

因为原始数据就还不错，就没有做sac
运行如右图结果：