C++_Eigen库的使用
介绍
Eigen是一个C++开源线性代数库。它提供了快速的有关矩阵的线性代数运算和解方程等功能。许多上层的软件库也使用Eigen进行矩阵运算,包括g2o、Sophus等.
下载
https://eigen.tuxfamily.org/index.php?title=Main_Page
windows下载zip后,进行解压缩
配置
CMakeList.txt配置
include_directories(D:\\C++_CLion\\SLAM_Test\\eigen)
填写解压路径
使用
#include "iostream"#include // 稠密矩阵的代数运算(逆、特征值)#include #include #define MATRTX_SIZE 50using namespace std;int main(int argc, char **argv) {//声明一个2x3的float矩阵Eigen::Matrix<float, 2, 3> matrix_23;//Vector3d实质上是Eigen::MatrixEigen::Vector3d v_3d;//Vector3d实质上是Eigen::MatrixEigen::Matrix3d matrix_33 = Eigen::Matrix3d::Zero();//不确定矩阵大小,可以使用动态矩阵Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic> matrix_dynamic;//或者Eigen::MatrixXd matrix_x;//矩阵输入操作matrix_23 << 1, 2, 3, 4, 5, 6;//输出cout << matrix_23 << endl;//类型cout << typeid(matrix_23).name() << endl;//访问矩阵中的元素for (int i = 0; i < 1; i++)for (int j = 0; j < 2; j++)cout << matrix_23(i, j) << endl;v_3d << 3, 2, 1;//矩阵和向量相乘, 不能混合两种不同类型的矩阵//Eigen::Matrix result_wrong = matrix_23 * v_3d;Eigen::Matrix<double, 2, 1> result = matrix_23.cast<double>() * v_3d;//矩阵运算matrix_33 = Eigen::Matrix3d::Random();cout << matrix_33 << endl << endl;cout << matrix_33.transpose() << endl; // 转置cout << matrix_33.trace() << endl; // 迹cout << 10 * matrix_33 << endl; // 数乘cout << matrix_33.inverse() << endl; // 逆cout << matrix_33.determinant() << endl; // 行列式//特征值//实对称矩阵可以保证对角化成功Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> eigen_solver(matrix_33.transpose() * matrix_33);cout << "Eigen values = " << eigen_solver.eigenvalues() << endl;cout << "Eigen vectors = " << eigen_solver.eigenvectors() << endl;//解方程//求解 matrix_NN * x = v_Nd//N大小为宏定义 矩阵由随机数生成Eigen::Matrix<double, MATRTX_SIZE, MATRTX_SIZE> matrix_NN;matrix_NN = Eigen::MatrixXd::Random(MATRTX_SIZE, MATRTX_SIZE);Eigen::Matrix<double, MATRTX_SIZE, 1> v_Nd;v_Nd = Eigen::MatrixXd::Random(MATRTX_SIZE, 1);clock_t time_start = clock(); //计时//直接求逆Eigen::Matrix<double, MATRTX_SIZE, 1> x = matrix_NN.inverse() * v_Nd;cout << "time use in normal inverse is " << 1000 * (clock() - time_start)/(double) CLOCKS_PER_SEC << "ms" << endl;//通常用矩阵分解求解 QR分解time_start = clock();x = matrix_NN.colPivHouseholderQr().solve(v_Nd);cout << "time use in normal inverse is " << 1000 * (clock() - time_start)/(double) CLOCKS_PER_SEC << "ms" << endl;return 0;}
1 2 34 5 6N5Eigen6MatrixIfLi2ELi3ELi0ELi2ELi3EEE12 -0.997497 0.617481-0.2994170.127171 0.170019 0.791925 -0.613392 -0.04025390.64568 -0.997497 0.127171-0.6133920.617481 0.170019 -0.0402539 -0.299417 0.7919250.64568-0.181799 -9.97497 6.17481-2.994171.27171 1.70019 7.91925 -6.13392 -0.4025396.4568-0.2715560.7412-1.035011.08862 1.58676-1.44134-0.1901080.8030590.475647-0.521644Eigen values = 0.145004 1.136 1.65193Eigen vectors =0.415633 0.0900561 -0.905063 0.906947 0.03394380.4198750.0685336 -0.995358 -0.067568time use in normal inverse is 2mstime use in normal inverse is 2ms进程已结束,退出代码0
来源—《视觉SLAM十四讲从理论到实践》