win10 VS2019 运行PCL tutorial ICP

ply模型下载：

https://www.cc.gatech.edu/projects/large_models/

或者自己用blender生成。

代码参考：http://www.pointclouds.org/documentation/tutorials/interactive_icp.php#interactive-icp

#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(int argc,
	char* argv[])
{
	// The point clouds we will be using
	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

			/*									  // Checking program arguments
	if (argc < 2)
	{
		printf("Usage :\n");
		printf("\t\t%s file.ply number_of_ICP_iterations\n", argv[0]);
		PCL_ERROR("Provide one ply file.\n");
		system("pause");
		return (-1);
	}*/

	int iterations = 1;  // Default number of ICP iterations
	/*
	if (argc > 2)
	{
		// If the user passed the number of iteration as an argument
		iterations = atoi(argv[2]);
		if (iterations < 1)
		{
			PCL_ERROR("Number of initial iterations must be >= 1\n");
			system("pause");
			return (-1);
		}
	}*/

	pcl::console::TicToc time;
	time.tic();
	if (pcl::io::loadPLYFile("C:/Users/Win10/Desktop/pcl_project/pcl_project/dragon.ply", *cloud_in) < 0)
	{
		PCL_ERROR("Error loading cloud %s.\n", "dragon.ply");
		system("pause");
		return (-1);
	}
	std::cout << "\nLoaded file " << "dragon.ply" << " (" << cloud_in->size() << " points) in " << time.toc() << " ms\n" << std::endl;

	// Defining a rotation matrix and translation vector
	Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity();
	cout << "a" << endl;
	// A rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)
	double theta = M_PI / 8;  // 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);

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

	// Display in terminal the transformation matrix
	std::cout << "Applying this rigid transformation to: cloud_in -> cloud_icp" << std::endl;
	print4x4Matrix(transformation_matrix);

	// Executing the transformation
	pcl::transformPointCloud(*cloud_in, *cloud_icp, transformation_matrix);
	*cloud_tr = *cloud_icp;  // We backup cloud_icp into cloud_tr for later use
	cout << "b" << endl;
							 // 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);
	}
	cout << "c" << endl;
	// 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);
	cout << "d" << endl;
	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);
}
           

问题1：“XXX被声明为已否决”

解决：直接双击属性表的根目录Debug|X64，将C/C++—>所有选项—>SDL检查改为否。

问题2：

第三方库flann报错：

添加头文件#include <pcl/registration/icp.h>编译的时候可能会遇到如下报错：

1>...\flann\algorithms\dist.h(523): error C3861: “pop_t”: 找不到标识符

解决：

（注由于要对第三方库的头文件进行修改，建议先备份一下原头文件，其路径...\PCL 1.9.0\3rdParty\FLANN\include\flann\algorithms\dist.h）

双击该行输出直接打开dist.h头文件；

将第503行的typedef unsigned long long pop_t;移动到第480行前面；保存。

由于数据量大，比较慢。