這次我們將學會怎么使用pcl::MomentOfInertiaEstimation?這個類來獲取以慣性矩和偏心距為基礎的描述器。這個類也能提取坐標對稱和定向包圍的方形盒子。但是記住導出的OBB不是最小可能性的盒子。

下面介紹了該種方法的特征提取方式。第一次先算出點云矩陣的協方差，計算它的特征值和特征向量。然后把特征向量進行歸一化處理，并把它組成右手坐標系。每一步都會迭代一次。每一次迭代特征向量都會旋轉。選轉的順序總是一樣的，總是被別的特征向量執行。這提供了選擇不變性。我們把這個旋轉的主向量作為當前的坐標系。

對于每一個慣性矩都會被計算。此外，當前的坐標系還被用于偏心距的計算。出于這個原因，當前的向量被當成一個平面的法線向量同時點云被投射到這個向量上。對于這個投射，偏心距會被計算。

完成上述實現的類還提供了方法來獲得AABB和OBB。旋轉的方形盒子被當做AABB和特征向量一起計算。

下面是一段代碼

#include <pcl/features/moment_of_inertia_estimation.h> #include <vector> #include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/visualization/cloud_viewer.h> #include <boost/thread/thread.hpp>int main (int argc, char** argv) {if (argc != 2)return (0); boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));viewer->setBackgroundColor (0, 0, 0);viewer->addCoordinateSystem (1.0);viewer->initCameraParameters ();viewer->addPointCloud<pcl::PointXYZ> (cloud, "sample cloud");viewer->addCube (min_point_AABB.x, max_point_AABB.x, min_point_AABB.y, max_point_AABB.y, min_point_AABB.z, max_point_AABB.z, 1.0, 1.0, 0.0, "AABB"); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ> ()); if (pcl::io::loadPCDFile (argv[1], *cloud) == -1) return (-1); pcl::MomentOfInertiaEstimation <pcl::PointXYZ> feature_extractor; feature_extractor.setInputCloud (cloud); feature_extractor.compute (); std::vector <float> moment_of_inertia; std::vector <float> eccentricity; pcl::PointXYZ min_point_AABB; pcl::PointXYZ max_point_AABB; pcl::PointXYZ min_point_OBB; pcl::PointXYZ max_point_OBB; pcl::PointXYZ position_OBB; Eigen::Matrix3f rotational_matrix_OBB; float major_value, middle_value, minor_value; Eigen::Vector3f major_vector, middle_vector, minor_vector; Eigen::Vector3f mass_center; feature_extractor.getMomentOfInertia (moment_of_inertia); feature_extractor.getEccentricity (eccentricity); feature_extractor.getAABB (min_point_AABB, max_point_AABB); feature_extractor.getOBB (min_point_OBB, max_point_OBB, position_OBB, rotational_matrix_OBB); feature_extractor.getEigenValues (major_value, middle_value, minor_value); feature_extractor.getEigenVectors (major_vector, middle_vector, minor_vector); feature_extractor.getMassCenter (mass_center); boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer")); viewer->setBackgroundColor (0, 0, 0); viewer->addCoordinateSystem (1.0); viewer->initCameraParameters (); viewer->addPointCloud<pcl::PointXYZ> (cloud, "sample cloud"); viewer->addCube (min_point_AABB.x, max_point_AABB.x, min_point_AABB.y, max_point_AABB.y, min_point_AABB.z, max_point_AABB.z, 1.0, 1.0, 0.0, "AABB"); Eigen::Vector3f position (position_OBB.x, position_OBB.y, position_OBB.z); Eigen::Quaternionf quat (rotational_matrix_OBB); viewer->addCube (position, quat, max_point_OBB.x - min_point_OBB.x, max_point_OBB.y - min_point_OBB.y, max_point_OBB.z - min_point_OBB.z, "OBB"); pcl::PointXYZ center (mass_center (0), mass_center (1), mass_center (2)); pcl::PointXYZ x_axis (major_vector (0) + mass_center (0), major_vector (1) + mass_center (1), major_vector (2) + mass_center (2)); pcl::PointXYZ y_axis (middle_vector (0) + mass_center (0), middle_vector (1) + mass_center (1), middle_vector (2) + mass_center (2)); pcl::PointXYZ z_axis (minor_vector (0) + mass_center (0), minor_vector (1) + mass_center (1), minor_vector (2) + mass_center (2)); viewer->addLine (center, x_axis, 1.0f, 0.0f, 0.0f, "major eigen vector"); viewer->addLine (center, y_axis, 0.0f, 1.0f, 0.0f, "middle eigen vector"); viewer->addLine (center, z_axis, 0.0f, 0.0f, 1.0f, "minor eigen vector"); //Eigen::Vector3f p1 (min_point_OBB.x, min_point_OBB.y, min_point_OBB.z); //Eigen::Vector3f p2 (min_point_OBB.x, min_point_OBB.y, max_point_OBB.z); //Eigen::Vector3f p3 (max_point_OBB.x, min_point_OBB.y, max_point_OBB.z); //Eigen::Vector3f p4 (max_point_OBB.x, min_point_OBB.y, min_point_OBB.z); //Eigen::Vector3f p5 (min_point_OBB.x, max_point_OBB.y, min_point_OBB.z); //Eigen::Vector3f p6 (min_point_OBB.x, max_point_OBB.y, max_point_OBB.z); //Eigen::Vector3f p7 (max_point_OBB.x, max_point_OBB.y, max_point_OBB.z); //Eigen::Vector3f p8 (max_point_OBB.x, max_point_OBB.y, min_point_OBB.z); //p1 = rotational_matrix_OBB * p1 + position; //p2 = rotational_matrix_OBB * p2 + position; //p3 = rotational_matrix_OBB * p3 + position; //p4 = rotational_matrix_OBB * p4 + position; //p5 = rotational_matrix_OBB * p5 + position; //p6 = rotational_matrix_OBB * p6 + position; //p7 = rotational_matrix_OBB * p7 + position; //p8 = rotational_matrix_OBB * p8 + position; //pcl::PointXYZ pt1 (p1 (0), p1 (1), p1 (2)); //pcl::PointXYZ pt2 (p2 (0), p2 (1), p2 (2)); //pcl::PointXYZ pt3 (p3 (0), p3 (1), p3 (2)); //pcl::PointXYZ pt4 (p4 (0), p4 (1), p4 (2)); //pcl::PointXYZ pt5 (p5 (0), p5 (1), p5 (2)); //pcl::PointXYZ pt6 (p6 (0), p6 (1), p6 (2)); //pcl::PointXYZ pt7 (p7 (0), p7 (1), p7 (2)); //pcl::PointXYZ pt8 (p8 (0), p8 (1), p8 (2)); //viewer->addLine (pt1, pt2, 1.0, 0.0, 0.0, "1 edge"); //viewer->addLine (pt1, pt4, 1.0, 0.0, 0.0, "2 edge"); //viewer->addLine (pt1, pt5, 1.0, 0.0, 0.0, "3 edge"); //viewer->addLine (pt5, pt6, 1.0, 0.0, 0.0, "4 edge"); //viewer->addLine (pt5, pt8, 1.0, 0.0, 0.0, "5 edge"); //viewer->addLine (pt2, pt6, 1.0, 0.0, 0.0, "6 edge"); //viewer->addLine (pt6, pt7, 1.0, 0.0, 0.0, "7 edge"); //viewer->addLine (pt7, pt8, 1.0, 0.0, 0.0, "8 edge"); //viewer->addLine (pt2, pt3, 1.0, 0.0, 0.0, "9 edge"); //viewer->addLine (pt4, pt8, 1.0, 0.0, 0.0, "10 edge"); //viewer->addLine (pt3, pt4, 1.0, 0.0, 0.0, "11 edge"); //viewer->addLine (pt3, pt7, 1.0, 0.0, 0.0, "12 edge"); while(!viewer->wasStopped()) { viewer->spinOnce (100); boost::this_thread::sleep (boost::posix_time::microseconds (100000)); } return (0);}

讓我們來對此解釋一下

pcl::MomentOfInertiaEstimation <pcl::PointXYZ> feature_extractor;feature_extractor.setInputCloud (cloud);feature_extractor.compute ();

上面的代碼加載了點云文件

std::vector <float> moment_of_inertia;std::vector <float> eccentricity;pcl::PointXYZ min_point_AABB;pcl::PointXYZ max_point_AABB;pcl::PointXYZ min_point_OBB;pcl::PointXYZ max_point_OBB;pcl::PointXYZ position_OBB;Eigen::Matrix3f rotational_matrix_OBB;float major_value, middle_value, minor_value;Eigen::Vector3f major_vector, middle_vector, minor_vector;Eigen::Vector3f mass_center;

上面是?pcl::MomentOfInertiaEstimation這個類實例化的代碼。

feature_extractor.getMomentOfInertia (moment_of_inertia);feature_extractor.getEccentricity (eccentricity);feature_extractor.getAABB (min_point_AABB, max_point_AABB);feature_extractor.getOBB (min_point_OBB, max_point_OBB, position_OBB, rotational_matrix_OBB);feature_extractor.getEigenValues (major_value, middle_value, minor_value);feature_extractor.getEigenVectors (major_vector, middle_vector, minor_vector);feature_extractor.getMassCenter (mass_center);

上面是我們聲明所有需要用來存儲描述器和方形盒子的變量。

boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));viewer->setBackgroundColor (0, 0, 0);viewer->addCoordinateSystem (1.0);viewer->initCameraParameters ();viewer->addPointCloud<pcl::PointXYZ> (cloud, "sample cloud");viewer->addCube (min_point_AABB.x, max_point_AABB.x, min_point_AABB.y, max_point_AABB.y, min_point_AABB.z, max_point_AABB.z, 1.0, 1.0, 0.0, "AABB");

上面展示了怎么獲取描述器和其它特征。

pcl::PointXYZ center (mass_center (0), mass_center (1), mass_center (2));pcl::PointXYZ x_axis (major_vector (0) + mass_center (0), major_vector (1) + mass_center (1), major_vector (2) + mass_center (2));pcl::PointXYZ y_axis (middle_vector (0) + mass_center (0), middle_vector (1) + mass_center (1), middle_vector (2) + mass_center (2));pcl::PointXYZ z_axis (minor_vector (0) + mass_center (0), minor_vector (1) + mass_center (1), minor_vector (2) + mass_center (2));viewer->addLine (center, x_axis, 1.0f, 0.0f, 0.0f, "major eigen vector");viewer->addLine (center, y_axis, 0.0f, 1.0f, 0.0f, "middle eigen vector");viewer->addLine (center, z_axis, 0.0f, 0.0f, 1.0f, "minor eigen vector");

上面簡單的創建了PCLVisualizer這個類，并把點云和AABB加入到可視化里面。

//Eigen::Vector3f p1 (min_point_OBB.x, min_point_OBB.y, min_point_OBB.z);//Eigen::Vector3f p2 (min_point_OBB.x, min_point_OBB.y, max_point_OBB.z);//Eigen::Vector3f p3 (max_point_OBB.x, min_point_OBB.y, max_point_OBB.z);//Eigen::Vector3f p4 (max_point_OBB.x, min_point_OBB.y, min_point_OBB.z);//Eigen::Vector3f p5 (min_point_OBB.x, max_point_OBB.y, min_point_OBB.z);//Eigen::Vector3f p6 (min_point_OBB.x, max_point_OBB.y, max_point_OBB.z);//Eigen::Vector3f p7 (max_point_OBB.x, max_point_OBB.y, max_point_OBB.z);//Eigen::Vector3f p8 (max_point_OBB.x, max_point_OBB.y, min_point_OBB.z);//p1 = rotational_matrix_OBB * p1 + position;//p2 = rotational_matrix_OBB * p2 + position;//p3 = rotational_matrix_OBB * p3 + position;//p4 = rotational_matrix_OBB * p4 + position;//p5 = rotational_matrix_OBB * p5 + position;//p6 = rotational_matrix_OBB * p6 + position;//p7 = rotational_matrix_OBB * p7 + position;//p8 = rotational_matrix_OBB * p8 + position;//pcl::PointXYZ pt1 (p1 (0), p1 (1), p1 (2));//pcl::PointXYZ pt2 (p2 (0), p2 (1), p2 (2));//pcl::PointXYZ pt3 (p3 (0), p3 (1), p3 (2));//pcl::PointXYZ pt4 (p4 (0), p4 (1), p4 (2));//pcl::PointXYZ pt5 (p5 (0), p5 (1), p5 (2));//pcl::PointXYZ pt6 (p6 (0), p6 (1), p6 (2));//pcl::PointXYZ pt7 (p7 (0), p7 (1), p7 (2));//pcl::PointXYZ pt8 (p8 (0), p8 (1), p8 (2));//viewer->addLine (pt1, pt2, 1.0, 0.0, 0.0, "1 edge");//viewer->addLine (pt1, pt4, 1.0, 0.0, 0.0, "2 edge");//viewer->addLine (pt1, pt5, 1.0, 0.0, 0.0, "3 edge");//viewer->addLine (pt5, pt6, 1.0, 0.0, 0.0, "4 edge");//viewer->addLine (pt5, pt8, 1.0, 0.0, 0.0, "5 edge");//viewer->addLine (pt2, pt6, 1.0, 0.0, 0.0, "6 edge");//viewer->addLine (pt6, pt7, 1.0, 0.0, 0.0, "7 edge");//viewer->addLine (pt7, pt8, 1.0, 0.0, 0.0, "8 edge");//viewer->addLine (pt2, pt3, 1.0, 0.0, 0.0, "9 edge");//viewer->addLine (pt4, pt8, 1.0, 0.0, 0.0, "10 edge");//viewer->addLine (pt3, pt4, 1.0, 0.0, 0.0, "11 edge");//viewer->addLine (pt3, pt7, 1.0, 0.0, 0.0, "12 edge");

上面是可以用來顯示特征向量的代碼。

這些大量的代碼展示了選擇的方形盒子是怎么工作的。記住你需要旋轉OBB的每一個頂點。這個代碼和PCLViser::addCube()方法一樣。

然后運行代碼

./moment_of_inertia lamppost.pcd

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總結

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