视觉SLAM理论与实践第四讲习题

结果：

CMakeLists.txt

cmake_minimum_required(VERSION 2.8)

project(undistort_image)

find_package(OpenCV 3.0 REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})

add_executable(undistort_image undistort_image.cpp)

target_link_libraries(undistort_image ${OpenCV_LIBS})

undistort_image.cpp

#include 
#include 

using namespace std;

string image_file = "./../test.png";   // 请确保路径正确

int main(int argc, char **argv)
{

    // 本程序需要你自己实现去畸变部分的代码。尽管我们可以调用OpenCV的去畸变，但自己实现一遍有助于理解。
    // 畸变参数
    double k1 = -0.28340811, k2 = 0.07395907, p1 = 0.00019359, p2 = 1.76187114e-05;
    // 内参
    double fx = 458.654, fy = 457.296, cx = 367.215, cy = 248.375;

    cv::Mat image = cv::imread(image_file,0);   // 图像是灰度图，CV_8UC1
    int rows = image.rows, cols = image.cols;
    cv::Mat image_undistort = cv::Mat(rows, cols, CV_8UC1);   // 去畸变以后的图

    // 计算去畸变后图像的内容
    for (int v = 0; v < rows; v++)
    {
        for (int u = 0; u < cols; u++)
        {

            double u_distorted = 0, v_distorted = 0;
            // TODO 按照公式，计算点(u,v)对应到畸变图像中的坐标(u_distorted, v_distorted) (~6 lines)
            // start your code here
            double x = (u - cx) / fx, y = (v - cy) / fy;
            double r = sqrt(x * x + y * y);
            u_distorted = x * (1 + k1 * r * r +k2 * r * r * r * r) + 2 * p1 * x * y + p2 * (r * r + 2 * x * x);
            v_distorted = y * (1 + k1 * r * r +k2 * r * r * r * r) + p1 * (r * r + 2 * y * y) + 2 * p2 * x * y;
            u_distorted = fx * u_distorted + cx;
            v_distorted = fy * v_distorted + cy;
            // end your code here

            // 赋值 (最近邻插值)
            if (u_distorted >= 0 && v_distorted >= 0 && u_distorted < cols && v_distorted < rows)
            {
                image_undistort.at(v, u) = image.at((int) v_distorted, (int) u_distorted);
            }
            else
            {
                image_undistort.at(v, u) = 0;
            }
        }
    }

    // 画图去畸变后图像
    cv::imshow("image undistorted", image_undistort);
    cv::imwrite("./../undistorted_image.jpg",image_undistort);
    cv::waitKey(0);

    return 0;
}

 

结果：

 

CMakeLists.txt

cmake_minimum_required(VERSION 2.8)

PROJECT(disparity)

IF(NOT CMAKE_BUILD_TYPE) #（可选）如果没有指定cmake编译模式，就选择Relealse模式，必须写成三行
   SET(CMAKE_BUILD_TYPE Release)
ENDIF()

MESSAGE("Build type: " ${CMAKE_BUILD_TYPE}) #终端打印cmake编译模式的信息

set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3 -march=native ") #添加c标准支持库
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall   -O3 -march=native") #添加c++标准支持库

# Check C++11 or C++0x support  #检查c++11或c++0x标准支持库
include(CheckCXXCompilerFlag)
CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
CHECK_CXX_COMPILER_FLAG("-std=c++0x" COMPILER_SUPPORTS_CXX0X)
if(COMPILER_SUPPORTS_CXX11)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
   add_definitions(-DCOMPILEDWITHC11)
   message(STATUS "Using flag -std=c++11.")
elseif(COMPILER_SUPPORTS_CXX0X)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x")
   add_definitions(-DCOMPILEDWITHC0X)
   message(STATUS "Using flag -std=c++0x.")
else()
   message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} has no C++11 support. Please use a different C++ compiler.")
endif()

find_package(OpenCV 3.0 QUIET) #find_package()命令首先会在模块路径中寻找 Find.cmake
if(NOT OpenCV_FOUND)
   find_package(OpenCV 2.4.3 QUIET)
   if(NOT OpenCV_FOUND)
       message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
   endif()
endif()
find_package(Pangolin REQUIRED)

include_directories("/usr/include/eigen3")
include_directories(
       ${OpenCV_INCLUDE_DIRS}
       ${Pangolin_INCLUDE_DIRS}
)
add_executable(disparity disparity.cpp)
#链接OpenCV库
target_link_libraries(disparity
       ${OpenCV_LIBS}
       ${Pangolin_LIBRARIES}
       )

disparity.cpp

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

using namespace std;
using namespace Eigen;

// 文件路径，如果不对，请调整
string left_file = "./../left.png";
string right_file = "./../right.png";
string disparity_file = "./../disparity.png";

// 在panglin中画图，已写好，无需调整
void showPointCloud(const vector> &pointcloud);

int main(int argc, char **argv) {

    // 内参
    double fx = 718.856, fy = 718.856, cx = 607.1928, cy = 185.2157;
    // 基线
    double b = 0.573;

    // 读取图像
    cv::Mat left = cv::imread(left_file, 0);
    cv::Mat right = cv::imread(right_file, 0);
    cv::Mat disparity = cv::imread(disparity_file, 0); // disparty 为CV_8U,单位为像素

    // 生成点云
    vector> pointcloud;
    // TODO 根据双目模型计算点云
    // 如果你的机器慢，请把后面的v++和u++改成v+=2, u+=2
    for (int v = 0; v < left.rows; v++)
        for (int u = 0; u < left.cols; u++) {

            Vector4d point(0, 0, 0, left.at(v, u) / 255.0); // 前三维为xyz,第四维为颜色

            // start your code here (~6 lines)
            // 根据双目模型计算 point 的位置
            unsigned short d = disparity.at(v,u);
            if(d == 0){
              continue;
            }
            double x = ( u - cx ) / fx;
            double y = ( v - cy ) / fy;
            double depth = ( fx * 1000 * b ) / d;

            point[0] = x * depth;
            point[1] = y * depth;
            point[2] = depth;

            pointcloud.push_back(point);

            // end your code here
        }

    // 画出点云
    showPointCloud(pointcloud);
    return 0;
}

void showPointCloud(const vector> &pointcloud) {

    if (pointcloud.empty()) {
        cerr << "Point cloud is empty!" << endl;
        return;
    }

    pangolin::CreateWindowAndBind("Point Cloud Viewer", 1024, 768);
    glEnable(GL_DEPTH_TEST);
    glEnable(GL_BLEND);
    glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);

    pangolin::OpenGlRenderState s_cam(
            pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000),
            pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0)
    );

    pangolin::View &d_cam = pangolin::CreateDisplay()
            .SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f)
            .SetHandler(new pangolin::Handler3D(s_cam));

    while (pangolin::ShouldQuit() == false) {
        glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);

        d_cam.Activate(s_cam);
        glClearColor(1.0f, 1.0f, 1.0f, 1.0f);

        glPointSize(2);
        glBegin(GL_POINTS);
        for (auto &p: pointcloud) {
            glColor3f(p[3], p[3], p[3]);
            glVertex3d(p[0], p[1], p[2]);
        }
        glEnd();
        pangolin::FinishFrame();
        usleep(5000);   // sleep 5 ms
    }
    return;
}

                                                                                                                          

 

 

 

结果：

CMakeLists.txt

cmake_minimum_required(VERSION 2.8)

PROJECT(gaussnewton)

find_package(Eigen3 3.1.0 REQUIRED)

find_package(OpenCV 3.0 REQUIRED)

#find_package(Pangolin REQUIRED)

include_directories( ${OpenCV_INCLUDE_DIRS} ${Eigen3_INCLUDE_DIRS})
#include_directories("/usr/include/eigen3")

add_executable(gaussnewton gaussnewton.cpp)

target_link_libraries(gaussnewton ${OpenCV_LIBS} ${EIGEN3_INCLUDE_DIRS} )

gaussnewton.cpp

#include 
#include 
#include 
#include 

using namespace std;
using namespace Eigen;

int main(int argc, char **argv) {
    double ar = 1.0, br = 2.0, cr = 1.0;         // 真实参数值
    double ae = 2.0, be = -1.0, ce = 5.0;        // 估计参数值
    int N = 100;                                 // 数据点
    double w_sigma = 1.0;                        // 噪声Sigma值
    cv::RNG rng;                                 // OpenCV随机数产生器

    vector x_data, y_data;      // 数据
    for (int i = 0; i < N; i++) {
        double x = i / 100.0;
        x_data.push_back(x);
        y_data.push_back(exp(ar * x * x + br * x + cr) + rng.gaussian(w_sigma));
        //生成100条随机噪声的数据（x_data,y_data）
    }

    // 开始Gauss-Newton迭代
    int iterations = 100;    // 迭代次数
    double cost = 0, lastCost = 0;  // 本次迭代的cost和上一次迭代的cost

    for (int iter = 0; iter < iterations; iter++) {

        Matrix3d H = Matrix3d::Zero();             // Hessian = J^T J in Gauss-Newton
        Vector3d b = Vector3d::Zero();             // bias
        cost = 0;

        for (int i = 0; i < N; i++) {
 double xi = x_data[i], yi = y_data[i];  // 第i个数据点
            // start your code here
            double error = 0;   // 第i个数据点的计算误差
            error = yi - exp(ae * xi * xi + be * xi +ce);
            // 填写计算error的表达式
            Vector3d J; // 雅可比矩阵
            J[0] = -xi * xi * exp(ae * xi *xi + be * xi + ce);  // de/da
            J[1] = -xi * exp(ae * xi *xi + be * xi + ce);  // de/db
            J[2] = -exp(ae * xi *xi + be * xi + ce);  // de/dc

            H += J * J.transpose(); // GN近似的H
            b += -error * J;
            // end your code here

            cost += error * error;
        }

        // 求解线性方程 Hx=b，建议用ldlt
        // start your code here
        Vector3d dx;
        dx = H.ldlt().solve(b);
        // end your code here

        if (isnan(dx[0])) {
            cout << "result is nan!" << endl;
            break;
        }

        if (iter > 0 && cost > lastCost) {
            // 误差增长了，说明近似的不够好
            cout << "cost: " << cost << ", last cost: " << lastCost << endl;
            break;
        }

        // 更新abc估计值
        ae += dx[0];
        be += dx[1];
        ce += dx[2];

        lastCost = cost;

        cout << "total cost: " << cost << endl;
    }

    cout << "estimated abc = " << ae << ", " << be << ", " << ce << endl;
    return 0;
}