点云处理---最小矩形包围盒

包围体是一个简单的几何空间，里面包含着复杂形状的物体。为物体添加包围体的目的是快速的进行碰撞检测或者进行精确的碰撞检测之前进行过滤（即当包围体碰撞，才进行精确碰撞检测和处理）。包围体类型包括球体、轴对齐包围盒（AABB）、有向包围盒（OBB）、8-DOP以及凸壳。

#include <iostream>
#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/project_inliers.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/passthrough.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/segmentation/extract_clusters.h>
#include <Eigen/Core>
#include <pcl/common/transforms.h>
#include <pcl/common/common.h>


using namespace std;
typedef pcl::PointXYZ PointType;

int main(int argc, char **argv)
{
    pcl::PointCloud<PointType>::Ptr cloud(new pcl::PointCloud<PointType>());
    pcl::io::loadPCDFile("table_scene_lms400.pcd", *cloud);

    Eigen::Vector4f pcaCentroid;
    pcl::compute3DCentroid(*cloud, pcaCentroid);
    Eigen::Matrix3f covariance;
    pcl::computeCovarianceMatrixNormalized(*cloud, pcaCentroid, covariance);
    Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eigen_solver(covariance, Eigen::ComputeEigenvectors);
    Eigen::Matrix3f eigenVectorsPCA = eigen_solver.eigenvectors();
    Eigen::Vector3f eigenValuesPCA = eigen_solver.eigenvalues();
    eigenVectorsPCA.col(2) = eigenVectorsPCA.col(0).cross(eigenVectorsPCA.col(1)); //校正主方向间垂直
    eigenVectorsPCA.col(0) = eigenVectorsPCA.col(1).cross(eigenVectorsPCA.col(2));
    eigenVectorsPCA.col(1) = eigenVectorsPCA.col(2).cross(eigenVectorsPCA.col(0));

    std::cout << "特征值va(3x1):\n" << eigenValuesPCA << std::endl;
    std::cout << "特征向量ve(3x3):\n" << eigenVectorsPCA << std::endl;
    std::cout << "质心点(4x1):\n" << pcaCentroid << std::endl;
    /*
    // 另一种计算点云协方差矩阵特征值和特征向量的方式:通过pcl中的pca接口，如下，这种情况得到的特征向量相似特征向量
    pcl::PointCloud::Ptr cloudPCAprojection (new pcl::PointCloud);
    pcl::PCA pca;
    pca.setInputCloud(cloudSegmented);
    pca.project(*cloudSegmented, *cloudPCAprojection);
    std::cerr << std::endl << "EigenVectors: " << pca.getEigenVectors() << std::endl;//计算特征向量
    std::cerr << std::endl << "EigenValues: " << pca.getEigenValues() << std::endl;//计算特征值
    */
    Eigen::Matrix4f tm = Eigen::Matrix4f::Identity();
    Eigen::Matrix4f tm_inv = Eigen::Matrix4f::Identity();
    tm.block<3, 3>(0, 0) = eigenVectorsPCA.transpose();   //R.
    tm.block<3, 1>(0, 3) = -1.0f * (eigenVectorsPCA.transpose()) *(pcaCentroid.head<3>());//  -R*t
    tm_inv = tm.inverse();

    std::cout << "变换矩阵tm(4x4):\n" << tm << std::endl;
    std::cout << "逆变矩阵tm'(4x4):\n" << tm_inv << std::endl;

    pcl::PointCloud<PointType>::Ptr transformedCloud(new pcl::PointCloud<PointType>);
    pcl::transformPointCloud(*cloud, *transformedCloud, tm);

    PointType min_p1, max_p1;   //点云的最大值与最小值点
    Eigen::Vector3f c1, c;
    pcl::getMinMax3D(*transformedCloud, min_p1, max_p1);
    c1 = 0.5f*(min_p1.getVector3fMap() + max_p1.getVector3fMap());

    std::cout << "型心c1(3x1):\n" << c1 << std::endl;

    Eigen::Affine3f tm_inv_aff(tm_inv);
    pcl::transformPoint(c1, c, tm_inv_aff);

    Eigen::Vector3f whd, whd1;
    whd1 = max_p1.getVector3fMap() - min_p1.getVector3fMap();
    whd = whd1;
    float sc1 = (whd1(0) + whd1(1) + whd1(2)) / 3;  //点云平均尺度，用于设置主方向箭头大小

    std::cout << "width1=" << whd1(0) << endl;
    std::cout << "heght1=" << whd1(1) << endl;
    std::cout << "depth1=" << whd1(2) << endl;
    std::cout << "scale1=" << sc1 << endl;

    const Eigen::Quaternionf bboxQ1(Eigen::Quaternionf::Identity());
    const Eigen::Vector3f    bboxT1(c1);

    const Eigen::Quaternionf bboxQ(tm_inv.block<3, 3>(0, 0));
    const Eigen::Vector3f    bboxT(c);


    //变换到原点的点云主方向
    PointType op;
    op.x = 0.0;
    op.y = 0.0;
    op.z = 0.0;
    Eigen::Vector3f px, py, pz;
    Eigen::Affine3f tm_aff(tm);
    pcl::transformVector(eigenVectorsPCA.col(0), px, tm_aff);
    pcl::transformVector(eigenVectorsPCA.col(1), py, tm_aff);
    pcl::transformVector(eigenVectorsPCA.col(2), pz, tm_aff);
    PointType pcaX;
    pcaX.x = sc1 * px(0);
    pcaX.y = sc1 * px(1);
    pcaX.z = sc1 * px(2);
    PointType pcaY;
    pcaY.x = sc1 * py(0);
    pcaY.y = sc1 * py(1);
    pcaY.z = sc1 * py(2);
    PointType pcaZ;
    pcaZ.x = sc1 * pz(0);
    pcaZ.y = sc1 * pz(1);
    pcaZ.z = sc1 * pz(2);

    //visualization
    pcl::visualization::PCLVisualizer viewer;


    pcl::visualization::PointCloudColorHandlerCustom<PointType> color_handler(cloud, 255, 255, 0); //输入的初始点云相关
    viewer.addPointCloud(cloud, color_handler, "cloud");
    viewer.addCube(bboxT, bboxQ, whd(0), whd(1), whd(2), "bbox");
    viewer.setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_REPRESENTATION,         pcl::visualization::PCL_VISUALIZER_REPRESENTATION_WIREFRAME, "bbox");
    viewer.setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 0.0, 0.0, "bbox");


    viewer.addCoordinateSystem(0.5f*sc1);
    viewer.setBackgroundColor(0.0, 0.0, 0.0);
    while (!viewer.wasStopped())
    {
          viewer.spinOnce();
    }

    return 0;
}