PCL Lesson5: 直通滤波+空间平面拟合+提供原始点云数据集PCD文件

书接上文。本节做下面一个工作：通过直通滤波过滤一小片平面区域的点云(标定版)，通过最小二乘法拟合，并把参数化的平面绘制在原图中。
待修正：拟合平面时离散点的处理。和拟合效果的判别

效果：

原始点云的文件这这里：（16线激光雷达，3.2w左右个点）
https://pan.baidu.com/s/1k6JOqbMgIFBJVz54bt3Ekg

#include 

#include
#include 
#include 
#include 
#include 
#include 

#include 
#include 
#include 


#define random(x1,x2) ((rand()%x2) - x1/2.0)

//Ax+by+cz=D  
void cvFitPlane(const CvMat* points, float* plane){
	// Estimate geometric centroid.  
	int nrows = points->rows;
	int ncols = points->cols;
	int type = points->type;
	CvMat* centroid = cvCreateMat(1, ncols, type);
	cvSet(centroid, cvScalar(0));
	for (int c = 0; c < ncols; c++){         //用地址的形式才能实现先列后行的操作，如果用Mat型只能先行后列
		for (int r = 0; r < nrows; r++)
		{
			centroid->data.fl[c] += points->data.fl[ncols*r + c];
		}
		centroid->data.fl[c] /= nrows;
	}
	// Subtract geometric centroid from each point.  
	CvMat* points2 = cvCreateMat(nrows, ncols, type);
	for (int r = 0; r < nrows; r++)
		for (int c = 0; c < ncols; c++)
			points2->data.fl[ncols*r + c] = points->data.fl[ncols*r + c] - centroid->data.fl[c];
	// Evaluate SVD of covariance matrix.  
	CvMat* A = cvCreateMat(ncols, ncols, type);
	CvMat* W = cvCreateMat(ncols, ncols, type);
	CvMat* V = cvCreateMat(ncols, ncols, type);
	cvGEMM(points2, points, 1, NULL, 0, A, CV_GEMM_A_T);  //points也要为CVMat型
	cvSVD(A, W, NULL, V, CV_SVD_V_T);
	// Assign plane coefficients by singular vector corresponding to smallest singular value.  
	plane[ncols] = 0;
	for (int c = 0; c < ncols; c++){
		plane[c] = V->data.fl[ncols*(ncols - 1) + c];
		plane[ncols] += plane[c] * centroid->data.fl[c];
	}
	// Release allocated resources.  
	cvReleaseMat(¢roid);
	cvReleaseMat(&points2);
	cvReleaseMat(&A);
	cvReleaseMat(&W);
	cvReleaseMat(&V);
}

int main()
{
	pcl::PointCloud::Ptr cloud2(new pcl::PointCloud); //PointXYZ 数据结构
	pcl::PointCloud::Ptr cloud_medium(new pcl::PointCloud);
	pcl::PCDReader reader;
	reader.read("pcdData//lader3.PCD",*cloud2);
	
	//x
	pcl::PassThrough pass_x;
	pass_x.setInputCloud(cloud2);
	pass_x.setFilterFieldName("x");
	pass_x.setFilterLimitsNegative(false);
	pass_x.setFilterLimits(5,7);
	pass_x.filter(*cloud_medium);
	//y
	pcl::PassThrough pass_y;
	pass_y.setInputCloud(cloud_medium);
	pass_y.setFilterFieldName("y");
	pass_y.setFilterLimitsNegative(false);
	pass_y.setFilterLimits(-1, 1);
	pass_y.filter(*cloud_medium);
	//z
	pcl::PassThrough pass_z;
	pass_z.setInputCloud(cloud_medium);
	pass_z.setFilterFieldName("z");
	pass_z.setFilterLimitsNegative(false);
	pass_z.setFilterLimits(-1.5, 1.5);
	pass_z.filter(*cloud_medium);

	
	//双视口
	int v1(0), v2(0);
	boost::shared_ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));
	
	viewer->createViewPort(0.0, 0.0, 0.5, 1.0, v1);//(Xmin,Ymin,Xmax,Ymax)设置窗口坐标
	viewer->setBackgroundColor(0, 0, 0, v1);
	viewer->addText("original", 10, 10, "v1 text", v1);//设置视口名称
	viewer->addPointCloud(cloud2, "sample cloud1", v1);//添加点云

	viewer->createViewPort(0.5, 0.0, 1.0, 1.0, v2);
	viewer->setBackgroundColor(0.3, 0.3, 0.3, v2);
	viewer->addText("after filtered", 10, 10, "v2 text", v2);
	viewer->addPointCloud(cloud_medium, "sample cloud2", v2);

	viewer->addCoordinateSystem(1.0,"sample cloud1");
	
	//
	CvMat*points_mat = cvCreateMat(cloud_medium->size(), 3, CV_32FC1);//定义用来存储需要拟合点的矩阵   
	float plane12[4] = { 0 };//定义用来储存平面参数的数组   
	printf("%d\n", cloud_medium->size());
	for (int i = 0; isize();i++)
	{
		points_mat->data.fl[i * 3 + 0] = cloud_medium->points[i].x;
		points_mat->data.fl[i * 3 + 1] = cloud_medium->points[i].y;//  Y的坐标值  
		points_mat->data.fl[i * 3 + 2] = cloud_medium->points[i].z;
	}
	cvFitPlane(points_mat, plane12);//调用方程   
	printf("A=%f\nB=%f\nC=%f\nD=%f\n", plane12[0], plane12[1], plane12[2], plane12[3]);

	pcl::ModelCoefficients plane_coeff;
	plane_coeff.values.resize(4);    // We need 4 values
	plane_coeff.values[0] = plane12[0];
	plane_coeff.values[1] = plane12[1];
	plane_coeff.values[2] = plane12[2];
	plane_coeff.values[3] = -plane12[3];

	viewer->addPlane(plane_coeff);
	while (!viewer->wasStopped())
	{

		viewer->spinOnce();
	}

	

	return 0;
}