PCL 基于惯性矩与偏心率的描述子进行包围盒提取

1、概述

本例程利用pcl::MomentOfInertiaEstimation类获取基于惯性矩(moment of inertia)与偏心率(eccentricity)的描述子，该类的另一功能是提取有向包围盒OBB(Oriented Bounding Box)和坐标轴对齐包围盒AABB(Axis-Aligned Bounding Box)。但是所提取的有向包围盒常用于物理模型或可视化中用到的碰撞检测等。

2、包围盒提取代码

#include <pcl/features/moment_of_inertia_estimation.h>//惯性矩估计的头文件pcl::MomentOfInertiaEstimation <pcl::PointXYZ> feature_extractor;//实例化一个对象feature_extractor.setInputCloud (cloud);//设置输入点云feature_extractor.compute ();//开始特征计算std::vector <float> moment_of_inertia;//存放惯性距的特征向量std::vector <float> eccentricity;//存放偏心率的特征向量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);//计算出的惯性矩feature_extractor.getEccentricity (eccentricity);//计算出的偏心率feature_extractor.getAABB (min_point_AABB, max_point_AABB);//计算轴对称边界盒子feature_extractor.getOBB (min_point_OBB, max_point_OBB, position_OBB, rotational_matrix_OBB);//OBB对应的相关参数feature_extractor.getEigenValues (major_value, middle_value, minor_value);//三个特征值feature_extractor.getEigenVectors (major_vector, middle_vector, minor_vector);//三个特征向量feature_extractor.getMassCenter (mass_center);//计算质心

3、全部代码

#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);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;//实例化一个对象feature_extractor.setInputCloud (cloud);//设置输入点云feature_extractor.compute ();//开始特征计算std::vector <float> moment_of_inertia;//存放惯性距的特征向量std::vector <float> eccentricity;//存放偏心率的特征向量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);//计算出的惯性矩feature_extractor.getEccentricity (eccentricity);//计算出的偏心率feature_extractor.getAABB (min_point_AABB, max_point_AABB);//计算轴对称边界盒子feature_extractor.getOBB (min_point_OBB, max_point_OBB, position_OBB, rotational_matrix_OBB);//OBB对应的相关参数feature_extractor.getEigenValues (major_value, middle_value, minor_value);//三个特征值feature_extractor.getEigenVectors (major_vector, middle_vector, minor_vector);//三个特征向量feature_extractor.getMassCenter (mass_center);//计算质心boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("点云库PCL学习教程第二版-基于惯性矩与偏心率的描述子"));viewer->setBackgroundColor (1, 1, 1);viewer->addCoordinateSystem (1.0);viewer->initCameraParameters ();viewer->addPointCloud<pcl::PointXYZ> (cloud,pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>(cloud,0,255,0), "sample cloud");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE,5,"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, 0.0, 0.0, "AABB");viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_OPACITY,0.1,"AABB");//图形的不透明度viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_LINE_WIDTH,4,"AABB");//线宽Eigen::Vector3f position (position_OBB.x, position_OBB.y, position_OBB.z);std::cout<<"position_OBB: "<<position_OBB<<endl;std::cout<<"mass_center: "<<mass_center<<endl;//中心坐标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");viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR,0,0,1,"OBB");viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_OPACITY,0.1,"OBB");viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_LINE_WIDTH,4,"OBB");viewer->setRepresentationToWireframeForAllActors();//将所有actor的可视化表示更改为线框表示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");std::cout<<"size of cloud :"<<cloud->points.size()<<endl;std::cout<<"moment_of_inertia :"<<moment_of_inertia.size()<<endl;std::cout<<"eccentricity :"<<eccentricity.size()<<endl;//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);
}

4、可视化

红色为坐标轴对齐包围盒AABB，蓝色为有向包围盒OBB

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PCL 基于惯性矩与偏心率的描述子进行包围盒提取

4、可视化

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