【PCL学习】点云配准——ICP

ICP可以说是最经典的方法之一了，这些天看论文下来很多方法后都要接一下ICP才能有一个比较好看的结果，故而来梳理下ICP的原理以及再看看基于PCL的ICP代码。

ICP在上个世纪[1]就被提出了,历久弥新2333，通过不断迭代使得两个点云进行配准。

========= 优化目标=============

三维空间中两个3D点，  ，他们的欧式距离表示为：

三维点云匹配问题的目的是找到P和Q变化的矩阵R和T，对于  ， ，利用最小二乘法求解最优解使：

最小时的R和T。

==========步骤=========

========================

效果图：（2ms迭代一次）小角度配准效果尚可，大角度（比如90°就GG了）

环境配置： 地址

#include <iostream>  
#include <string>  
#include <pcl/io/ply_io.h>  
#include <pcl/point_types.h>  
#include <pcl/registration/icp.h>  
#include <pcl/visualization/pcl_visualizer.h>  
#include <pcl/console/time.h>   // TicToc  

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

bool next_iteration = false;

void print4x4Matrix(const Eigen::Matrix4d & matrix)
{
	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)
{
	if (event.getKeySym() == "space" && event.keyDown())
		next_iteration = true;
}

int main()
{
	// 声明需要用到的点云（读入的，转换的，ICP调整的三个点云）
	PointCloudT::Ptr cloud_in(new PointCloudT);  // Original point cloud  
	PointCloudT::Ptr cloud_tr(new PointCloudT);  // Transformed point cloud  
	PointCloudT::Ptr cloud_icp(new PointCloudT);  // ICP output point cloud  

	int iterations = 0;  // Default number of ICP iterations  
						

	pcl::console::TicToc time;
	time.tic();
	std::string filename = "cow-2.ply";
	if (pcl::io::loadPLYFile(filename, *cloud_in) < 0)
	{
		PCL_ERROR("Error loading cloud %s.\n", filename);
		system("pause");
		return (-1);
	}

	std::cout << "\nLoaded file " << filename << " (" << cloud_in->size() << " points) in " << time.toc() << " ms\n" << std::endl;

	// 定义旋转平移的转换矩阵
	Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity();

	// A rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)  
	double theta = M_PI/4 ;  // The angle of rotation in radians  
	transformation_matrix(0, 0) = cos(theta);
	transformation_matrix(0, 1) = -sin(theta);
	transformation_matrix(1, 0) = sin(theta);
	transformation_matrix(1, 1) = cos(theta);

	//Z轴平移0.4米
	// A translation on Z axis (0.4 meters)  
	transformation_matrix(2, 3) = 0.4;
	

	//打印出旋转矩阵R和平移T
	std::cout << "Applying this rigid transformation to: cloud_in -> cloud_icp" << std::endl;
	print4x4Matrix(transformation_matrix);

	//转移后
	pcl::transformPointCloud(*cloud_in, *cloud_icp, transformation_matrix);
	*cloud_tr = *cloud_icp;  // We backup cloud_icp into cloud_tr for later use  

							 // The Iterative Closest Point algorithm  
	time.tic();
	pcl::IterativeClosestPoint<PointT, PointT> icp;
	icp.setMaximumIterations(iterations);
	icp.setInputSource(cloud_icp);
	icp.setInputTarget(cloud_in);
	icp.align(*cloud_icp);
	icp.setMaximumIterations(1);  // We set this variable to 1 for the next time we will call .align () function  
	std::cout << "Applied " << iterations << " ICP iteration(s) in " << time.toc() << " ms" << std::endl;

	if (icp.hasConverged())
	{
		std::cout << "\nICP has converged, score is " << icp.getFitnessScore() << std::endl;
		std::cout << "\nICP transformation " << iterations << " : cloud_icp -> cloud_in" << std::endl;
		transformation_matrix = icp.getFinalTransformation().cast<double>();
		print4x4Matrix(transformation_matrix);
	}
	else
	{
		PCL_ERROR("\nICP has not converged.\n");
		system("pause");
		return (-1);
	}

	// Visualization  
	pcl::visualization::PCLVisualizer viewer("ICP demo");
	// Create two vertically separated viewports  
	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);

	// The color we will be using  
	float bckgr_gray_level = 0.0;  // Black  
	float txt_gray_lvl = 1.0 - bckgr_gray_level;

	// Original point cloud is white  
	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);
	viewer.addPointCloud(cloud_in, cloud_in_color_h, "cloud_in_v2", v2);

	// Transformed point cloud is green  
	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 aligned point cloud is red  
	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);

	// Adding text descriptions in each viewport  
	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;
	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);

	// Set background color  
	viewer.setBackgroundColor(bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v1);
	viewer.setBackgroundColor(bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v2);

	// Set camera position and orientation  
	viewer.setCameraPosition(-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
	viewer.setSize(1280, 1024);  // Visualiser window size  

								 // Register keyboard callback :  
	viewer.registerKeyboardCallback(&keyboardEventOccurred, (void*)NULL);

	// Display the visualiser  
	while (!viewer.wasStopped())
	{
		viewer.spinOnce();

		// The user pressed "space" :  
		if (next_iteration)
		{
			// The Iterative Closest Point algorithm  
			time.tic();
			icp.align(*cloud_icp);
			std::cout << "Applied 1 ICP iteration in " << time.toc() << " ms" << std::endl;

			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;
				transformation_matrix *= icp.getFinalTransformation().cast<double>();  
				// WARNING /!\ This is not accurate! For "educational" purpose only!

				print4x4Matrix(transformation_matrix);  // Print the transformation between original pose and current pose  

				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");
				system("pause");
				return (-1);
			}
		}
		next_iteration = false;
	}
	system("pause");
	return (0);
}

Reference

[1]Zhang Z. Iterative point matching for registration of free-form curves and surfaces[J]. International Journal of Computer Vision, 1994, 13(2):119-152.

[2] ICP相关

[3] ICP参考2

[4] ICP参考3

[5] ICP参考4