视觉SLAM十四讲从理论到实践第二版源码调试笔记（实践应用7-14章）

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视觉SLAM十四讲从理论到实践第二版源码调试笔记（理论基础1-6章）

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第七章和第八章：视觉里程计 1+2

使用示例，需要OpenCV4，报错如下：

ROS:~/SLAM/slambook2/ch8/build$ cmake ..
-- The C compiler identification is GNU 7.4.0
-- The CXX compiler identification is GNU 7.4.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
CMake Error at CMakeLists.txt:8 (find_package):
  Could not find a configuration file for package "OpenCV" that is compatible
  with requested version "4".

  The following configuration files were considered but not accepted:

    /usr/share/OpenCV/OpenCVConfig.cmake, version: 3.2.0



-- Configuring incomplete, errors occurred!
See also "/home/relaybot/SLAM/slambook2/ch8/build/CMakeFiles/CMakeOutput.log".

安装OpenCV4参考：Ubuntu安装OpenCV4记录

ROS:~/SLAM/slambook2/ch8/build$ cmake ..
-- The C compiler identification is GNU 7.4.0
-- The CXX compiler identification is GNU 7.4.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found OpenCV: /usr/local (found suitable version "4.1.2", minimum required is "4") 
-- Configuring done
-- Generating done
-- Build files have been written to: /home/relaybot/SLAM/slambook2/ch8/build
ROS:~/SLAM/slambook2/ch8/build$ make

编译时如果报错，分别如下：

optical_flow.cpp：

$ make
Scanning dependencies of target optical_flow
[ 25%] Building CXX object CMakeFiles/optical_flow.dir/optical_flow.cpp.o
/home/relaybot/SLAM/slambook2/ch8/optical_flow.cpp: In function ‘int main(int, char**)’:
/home/relaybot/SLAM/slambook2/ch8/optical_flow.cpp:143:37: error: ‘CV_GRAY2BGR’ was not declared in this scope
     cv::cvtColor(img2, img2_single, CV_GRAY2BGR);
                                     ^~~~~~~~~~~
CMakeFiles/optical_flow.dir/build.make:62: recipe for target 'CMakeFiles/optical_flow.dir/optical_flow.cpp.o' failed
make[2]: *** [CMakeFiles/optical_flow.dir/optical_flow.cpp.o] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/optical_flow.dir/all' failed
make[1]: *** [CMakeFiles/optical_flow.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2

将CV_GRAY2BGR，更新为COLOR_GRAY2BGR。

//
// Created by Xiang on 2017/12/19.
//

#include 
#include 
#include 
#include 
#include 

using namespace std;
using namespace cv;

string file_1 = "./LK1.png";  // first image
string file_2 = "./LK2.png";  // second image

/// Optical flow tracker and interface
class OpticalFlowTracker {
public:
    OpticalFlowTracker(
        const Mat &img1_,
        const Mat &img2_,
        const vector &kp1_,
        vector &kp2_,
        vector &success_,
        bool inverse_ = true, bool has_initial_ = false) :
        img1(img1_), img2(img2_), kp1(kp1_), kp2(kp2_), success(success_), inverse(inverse_),
        has_initial(has_initial_) {}

    void calculateOpticalFlow(const Range &range);

private:
    const Mat &img1;
    const Mat &img2;
    const vector &kp1;
    vector &kp2;
    vector &success;
    bool inverse = true;
    bool has_initial = false;
};

/**
 * single level optical flow
 * @param [in] img1 the first image
 * @param [in] img2 the second image
 * @param [in] kp1 keypoints in img1
 * @param [in|out] kp2 keypoints in img2, if empty, use initial guess in kp1
 * @param [out] success true if a keypoint is tracked successfully
 * @param [in] inverse use inverse formulation?
 */
void OpticalFlowSingleLevel(
    const Mat &img1,
    const Mat &img2,
    const vector &kp1,
    vector &kp2,
    vector &success,
    bool inverse = false,
    bool has_initial_guess = false
);

/**
 * multi level optical flow, scale of pyramid is set to 2 by default
 * the image pyramid will be create inside the function
 * @param [in] img1 the first pyramid
 * @param [in] img2 the second pyramid
 * @param [in] kp1 keypoints in img1
 * @param [out] kp2 keypoints in img2
 * @param [out] success true if a keypoint is tracked successfully
 * @param [in] inverse set true to enable inverse formulation
 */
void OpticalFlowMultiLevel(
    const Mat &img1,
    const Mat &img2,
    const vector &kp1,
    vector &kp2,
    vector &success,
    bool inverse = false
);

/**
 * get a gray scale value from reference image (bi-linear interpolated)
 * @param img
 * @param x
 * @param y
 * @return the interpolated value of this pixel
 */
inline float GetPixelValue(const cv::Mat &img, float x, float y) {
    // boundary check
    if (x < 0) x = 0;
    if (y < 0) y = 0;
    if (x >= img.cols) x = img.cols - 1;
    if (y >= img.rows) y = img.rows - 1;
    uchar *data = &img.data[int(y) * img.step + int(x)];
    float xx = x - floor(x);
    float yy = y - floor(y);
    return float(
        (1 - xx) * (1 - yy) * data[0] +
        xx * (1 - yy) * data[1] +
        (1 - xx) * yy * data[img.step] +
        xx * yy * data[img.step + 1]
    );
}

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

    // images, note they are CV_8UC1, not CV_8UC3
    Mat img1 = imread(file_1, 0);
    Mat img2 = imread(file_2, 0);

    // key points, using GFTT here.
    vector kp1;
    Ptr detector = GFTTDetector::create(500, 0.01, 20); // maximum 500 keypoints
    detector->detect(img1, kp1);

    // now lets track these key points in the second image
    // first use single level LK in the validation picture
    vector kp2_single;
    vector success_single;
    OpticalFlowSingleLevel(img1, img2, kp1, kp2_single, success_single);

    // then test multi-level LK
    vector kp2_multi;
    vector success_multi;
    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
    OpticalFlowMultiLevel(img1, img2, kp1, kp2_multi, success_multi, true);
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    auto time_used = chrono::duration_cast>(t2 - t1);
    cout << "optical flow by gauss-newton: " << time_used.count() << endl;

    // use opencv's flow for validation
    vector pt1, pt2;
    for (auto &kp: kp1) pt1.push_back(kp.pt);
    vector status;
    vector error;
    t1 = chrono::steady_clock::now();
    cv::calcOpticalFlowPyrLK(img1, img2, pt1, pt2, status, error);
    t2 = chrono::steady_clock::now();
    time_used = chrono::duration_cast>(t2 - t1);
    cout << "optical flow by opencv: " << time_used.count() << endl;

    // plot the differences of those functions
    Mat img2_single;
    cv::cvtColor(img2, img2_single, COLOR_GRAY2BGR);
    for (int i = 0; i < kp2_single.size(); i++) {
        if (success_single[i]) {
            cv::circle(img2_single, kp2_single[i].pt, 2, cv::Scalar(0, 250, 0), 2);
            cv::line(img2_single, kp1[i].pt, kp2_single[i].pt, cv::Scalar(0, 250, 0));
        }
    }

    Mat img2_multi;
    cv::cvtColor(img2, img2_multi, COLOR_GRAY2BGR);
    for (int i = 0; i < kp2_multi.size(); i++) {
        if (success_multi[i]) {
            cv::circle(img2_multi, kp2_multi[i].pt, 2, cv::Scalar(0, 250, 0), 2);
            cv::line(img2_multi, kp1[i].pt, kp2_multi[i].pt, cv::Scalar(0, 250, 0));
        }
    }

    Mat img2_CV;
    cv::cvtColor(img2, img2_CV, COLOR_GRAY2BGR);
    for (int i = 0; i < pt2.size(); i++) {
        if (status[i]) {
            cv::circle(img2_CV, pt2[i], 2, cv::Scalar(0, 250, 0), 2);
            cv::line(img2_CV, pt1[i], pt2[i], cv::Scalar(0, 250, 0));
        }
    }

    cv::imshow("tracked single level", img2_single);
    cv::imshow("tracked multi level", img2_multi);
    cv::imshow("tracked by opencv", img2_CV);
    cv::waitKey(0);

    return 0;
}

void OpticalFlowSingleLevel(
    const Mat &img1,
    const Mat &img2,
    const vector &kp1,
    vector &kp2,
    vector &success,
    bool inverse, bool has_initial) {
    kp2.resize(kp1.size());
    success.resize(kp1.size());
    OpticalFlowTracker tracker(img1, img2, kp1, kp2, success, inverse, has_initial);
    parallel_for_(Range(0, kp1.size()),
                  std::bind(&OpticalFlowTracker::calculateOpticalFlow, &tracker, placeholders::_1));
}

void OpticalFlowTracker::calculateOpticalFlow(const Range &range) {
    // parameters
    int half_patch_size = 4;
    int iterations = 10;
    for (size_t i = range.start; i < range.end; i++) {
        auto kp = kp1[i];
        double dx = 0, dy = 0; // dx,dy need to be estimated
        if (has_initial) {
            dx = kp2[i].pt.x - kp.pt.x;
            dy = kp2[i].pt.y - kp.pt.y;
        }

        double cost = 0, lastCost = 0;
        bool succ = true; // indicate if this point succeeded

        // Gauss-Newton iterations
        Eigen::Matrix2d H = Eigen::Matrix2d::Zero();    // hessian
        Eigen::Vector2d b = Eigen::Vector2d::Zero();    // bias
        Eigen::Vector2d J;  // jacobian
        for (int iter = 0; iter < iterations; iter++) {
            if (inverse == false) {
                H = Eigen::Matrix2d::Zero();
                b = Eigen::Vector2d::Zero();
            } else {
                // only reset b
                b = Eigen::Vector2d::Zero();
            }

            cost = 0;

            // compute cost and jacobian
            for (int x = -half_patch_size; x < half_patch_size; x++)
                for (int y = -half_patch_size; y < half_patch_size; y++) {
                    double error = GetPixelValue(img1, kp.pt.x + x, kp.pt.y + y) -
                                   GetPixelValue(img2, kp.pt.x + x + dx, kp.pt.y + y + dy);;  // Jacobian
                    if (inverse == false) {
                        J = -1.0 * Eigen::Vector2d(
                            0.5 * (GetPixelValue(img2, kp.pt.x + dx + x + 1, kp.pt.y + dy + y) -
                                   GetPixelValue(img2, kp.pt.x + dx + x - 1, kp.pt.y + dy + y)),
                            0.5 * (GetPixelValue(img2, kp.pt.x + dx + x, kp.pt.y + dy + y + 1) -
                                   GetPixelValue(img2, kp.pt.x + dx + x, kp.pt.y + dy + y - 1))
                        );
                    } else if (iter == 0) {
                        // in inverse mode, J keeps same for all iterations
                        // NOTE this J does not change when dx, dy is updated, so we can store it and only compute error
                        J = -1.0 * Eigen::Vector2d(
                            0.5 * (GetPixelValue(img1, kp.pt.x + x + 1, kp.pt.y + y) -
                                   GetPixelValue(img1, kp.pt.x + x - 1, kp.pt.y + y)),
                            0.5 * (GetPixelValue(img1, kp.pt.x + x, kp.pt.y + y + 1) -
                                   GetPixelValue(img1, kp.pt.x + x, kp.pt.y + y - 1))
                        );
                    }
                    // compute H, b and set cost;
                    b += -error * J;
                    cost += error * error;
                    if (inverse == false || iter == 0) {
                        // also update H
                        H += J * J.transpose();
                    }
                }

            // compute update
            Eigen::Vector2d update = H.ldlt().solve(b);

            if (std::isnan(update[0])) {
                // sometimes occurred when we have a black or white patch and H is irreversible
                cout << "update is nan" << endl;
                succ = false;
                break;
            }

            if (iter > 0 && cost > lastCost) {
                break;
            }

            // update dx, dy
            dx += update[0];
            dy += update[1];
            lastCost = cost;
            succ = true;

            if (update.norm() < 1e-2) {
                // converge
                break;
            }
        }

        success[i] = succ;

        // set kp2
        kp2[i].pt = kp.pt + Point2f(dx, dy);
    }
}

void OpticalFlowMultiLevel(
    const Mat &img1,
    const Mat &img2,
    const vector &kp1,
    vector &kp2,
    vector &success,
    bool inverse) {

    // parameters
    int pyramids = 4;
    double pyramid_scale = 0.5;
    double scales[] = {1.0, 0.5, 0.25, 0.125};

    // create pyramids
    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
    vector pyr1, pyr2; // image pyramids
    for (int i = 0; i < pyramids; i++) {
        if (i == 0) {
            pyr1.push_back(img1);
            pyr2.push_back(img2);
        } else {
            Mat img1_pyr, img2_pyr;
            cv::resize(pyr1[i - 1], img1_pyr,
                       cv::Size(pyr1[i - 1].cols * pyramid_scale, pyr1[i - 1].rows * pyramid_scale));
            cv::resize(pyr2[i - 1], img2_pyr,
                       cv::Size(pyr2[i - 1].cols * pyramid_scale, pyr2[i - 1].rows * pyramid_scale));
            pyr1.push_back(img1_pyr);
            pyr2.push_back(img2_pyr);
        }
    }
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    auto time_used = chrono::duration_cast>(t2 - t1);
    cout << "build pyramid time: " << time_used.count() << endl;

    // coarse-to-fine LK tracking in pyramids
    vector kp1_pyr, kp2_pyr;
    for (auto &kp:kp1) {
        auto kp_top = kp;
        kp_top.pt *= scales[pyramids - 1];
        kp1_pyr.push_back(kp_top);
        kp2_pyr.push_back(kp_top);
    }

    for (int level = pyramids - 1; level >= 0; level--) {
        // from coarse to fine
        success.clear();
        t1 = chrono::steady_clock::now();
        OpticalFlowSingleLevel(pyr1[level], pyr2[level], kp1_pyr, kp2_pyr, success, inverse, true);
        t2 = chrono::steady_clock::now();
        auto time_used = chrono::duration_cast>(t2 - t1);
        cout << "track pyr " << level << " cost time: " << time_used.count() << endl;

        if (level > 0) {
            for (auto &kp: kp1_pyr)
                kp.pt /= pyramid_scale;
            for (auto &kp: kp2_pyr)
                kp.pt /= pyramid_scale;
        }
    }

    for (auto &kp: kp2_pyr)
        kp2.push_back(kp);
}

但是，direct_method.cpp依然报错，如下：

make
[ 50%] Built target optical_flow
Scanning dependencies of target direct_method
[ 75%] Building CXX object CMakeFiles/direct_method.dir/direct_method.cpp.o
/home/relaybot/SLAM/slambook2/ch8/direct_method.cpp: In function ‘void DirectPoseEstimationSingleLayer(const cv::Mat&, const cv::Mat&, const VecVector2d&, std::vector >, Sophus::SE3d&)’:
/home/relaybot/SLAM/slambook2/ch8/direct_method.cpp:206:35: error: ‘COLOR_GRAY2BGR’ was not declared in this scope
     cv::cvtColor(img2, img2_show, COLOR_GRAY2BGR);
                                   ^~~~~~~~~~~~~~
/home/relaybot/SLAM/slambook2/ch8/direct_method.cpp:206:35: note: suggested alternative:
In file included from /usr/local/include/opencv4/opencv2/opencv.hpp:74:0,
                 from /home/relaybot/SLAM/slambook2/ch8/direct_method.cpp:1:
/usr/local/include/opencv4/opencv2/imgproc.hpp:542:5: note:   ‘COLOR_GRAY2BGR’
     COLOR_GRAY2BGR     = 8,
     ^~~~~~~~~~~~~~
CMakeFiles/direct_method.dir/build.make:62: recipe for target 'CMakeFiles/direct_method.dir/direct_method.cpp.o' failed
make[2]: *** [CMakeFiles/direct_method.dir/direct_method.cpp.o] Error 1
CMakeFiles/Makefile2:104: recipe for target 'CMakeFiles/direct_method.dir/all' failed
make[1]: *** [CMakeFiles/direct_method.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2

在程序中，添加using namespace cv;

修正后的程序如下：

#include 
#include 
#include 
#include 

using namespace std;
using namespace cv;

typedef vector> VecVector2d;

// Camera intrinsics
double fx = 718.856, fy = 718.856, cx = 607.1928, cy = 185.2157;
// baseline
double baseline = 0.573;
// paths
string left_file = "./left.png";
string disparity_file = "./disparity.png";
boost::format fmt_others("./%06d.png");    // other files

// useful typedefs
typedef Eigen::Matrix Matrix6d;
typedef Eigen::Matrix Matrix26d;
typedef Eigen::Matrix Vector6d;

/// class for accumulator jacobians in parallel
class JacobianAccumulator {
public:
    JacobianAccumulator(
        const cv::Mat &img1_,
        const cv::Mat &img2_,
        const VecVector2d &px_ref_,
        const vector depth_ref_,
        Sophus::SE3d &T21_) :
        img1(img1_), img2(img2_), px_ref(px_ref_), depth_ref(depth_ref_), T21(T21_) {
        projection = VecVector2d(px_ref.size(), Eigen::Vector2d(0, 0));
    }

    /// accumulate jacobians in a range
    void accumulate_jacobian(const cv::Range &range);

    /// get hessian matrix
    Matrix6d hessian() const { return H; }

    /// get bias
    Vector6d bias() const { return b; }

    /// get total cost
    double cost_func() const { return cost; }

    /// get projected points
    VecVector2d projected_points() const { return projection; }

    /// reset h, b, cost to zero
    void reset() {
        H = Matrix6d::Zero();
        b = Vector6d::Zero();
        cost = 0;
    }

private:
    const cv::Mat &img1;
    const cv::Mat &img2;
    const VecVector2d &px_ref;
    const vector depth_ref;
    Sophus::SE3d &T21;
    VecVector2d projection; // projected points

    std::mutex hessian_mutex;
    Matrix6d H = Matrix6d::Zero();
    Vector6d b = Vector6d::Zero();
    double cost = 0;
};

/**
 * pose estimation using direct method
 * @param img1
 * @param img2
 * @param px_ref
 * @param depth_ref
 * @param T21
 */
void DirectPoseEstimationMultiLayer(
    const cv::Mat &img1,
    const cv::Mat &img2,
    const VecVector2d &px_ref,
    const vector depth_ref,
    Sophus::SE3d &T21
);

/**
 * pose estimation using direct method
 * @param img1
 * @param img2
 * @param px_ref
 * @param depth_ref
 * @param T21
 */
void DirectPoseEstimationSingleLayer(
    const cv::Mat &img1,
    const cv::Mat &img2,
    const VecVector2d &px_ref,
    const vector depth_ref,
    Sophus::SE3d &T21
);

// bilinear interpolation
inline float GetPixelValue(const cv::Mat &img, float x, float y) {
    // boundary check
    if (x < 0) x = 0;
    if (y < 0) y = 0;
    if (x >= img.cols) x = img.cols - 1;
    if (y >= img.rows) y = img.rows - 1;
    uchar *data = &img.data[int(y) * img.step + int(x)];
    float xx = x - floor(x);
    float yy = y - floor(y);
    return float(
        (1 - xx) * (1 - yy) * data[0] +
        xx * (1 - yy) * data[1] +
        (1 - xx) * yy * data[img.step] +
        xx * yy * data[img.step + 1]
    );
}

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

    cv::Mat left_img = cv::imread(left_file, 0);
    cv::Mat disparity_img = cv::imread(disparity_file, 0);

    // let's randomly pick pixels in the first image and generate some 3d points in the first image's frame
    cv::RNG rng;
    int nPoints = 2000;
    int boarder = 20;
    VecVector2d pixels_ref;
    vector depth_ref;

    // generate pixels in ref and load depth data
    for (int i = 0; i < nPoints; i++) {
        int x = rng.uniform(boarder, left_img.cols - boarder);  // don't pick pixels close to boarder
        int y = rng.uniform(boarder, left_img.rows - boarder);  // don't pick pixels close to boarder
        int disparity = disparity_img.at(y, x);
        double depth = fx * baseline / disparity; // you know this is disparity to depth
        depth_ref.push_back(depth);
        pixels_ref.push_back(Eigen::Vector2d(x, y));
    }

    // estimates 01~05.png's pose using this information
    Sophus::SE3d T_cur_ref;

    for (int i = 1; i < 6; i++) {  // 1~10
        cv::Mat img = cv::imread((fmt_others % i).str(), 0);
        // try single layer by uncomment this line
        // DirectPoseEstimationSingleLayer(left_img, img, pixels_ref, depth_ref, T_cur_ref);
        DirectPoseEstimationMultiLayer(left_img, img, pixels_ref, depth_ref, T_cur_ref);
    }
    return 0;
}

void DirectPoseEstimationSingleLayer(
    const cv::Mat &img1,
    const cv::Mat &img2,
    const VecVector2d &px_ref,
    const vector depth_ref,
    Sophus::SE3d &T21) {

    const int iterations = 10;
    double cost = 0, lastCost = 0;
    auto t1 = chrono::steady_clock::now();
    JacobianAccumulator jaco_accu(img1, img2, px_ref, depth_ref, T21);

    for (int iter = 0; iter < iterations; iter++) {
        jaco_accu.reset();
        cv::parallel_for_(cv::Range(0, px_ref.size()),
                          std::bind(&JacobianAccumulator::accumulate_jacobian, &jaco_accu, std::placeholders::_1));
        Matrix6d H = jaco_accu.hessian();
        Vector6d b = jaco_accu.bias();

        // solve update and put it into estimation
        Vector6d update = H.ldlt().solve(b);;
        T21 = Sophus::SE3d::exp(update) * T21;
        cost = jaco_accu.cost_func();

        if (std::isnan(update[0])) {
            // sometimes occurred when we have a black or white patch and H is irreversible
            cout << "update is nan" << endl;
            break;
        }
        if (iter > 0 && cost > lastCost) {
            cout << "cost increased: " << cost << ", " << lastCost << endl;
            break;
        }
        if (update.norm() < 1e-3) {
            // converge
            break;
        }

        lastCost = cost;
        cout << "iteration: " << iter << ", cost: " << cost << endl;
    }

    cout << "T21 = \n" << T21.matrix() << endl;
    auto t2 = chrono::steady_clock::now();
    auto time_used = chrono::duration_cast>(t2 - t1);
    cout << "direct method for single layer: " << time_used.count() << endl;

    // plot the projected pixels here
    cv::Mat img2_show;
    //Mat img2_show;
    cv::cvtColor(img2, img2_show, COLOR_GRAY2BGR);
    VecVector2d projection = jaco_accu.projected_points();
    for (size_t i = 0; i < px_ref.size(); ++i) {
        auto p_ref = px_ref[i];
        auto p_cur = projection[i];
        if (p_cur[0] > 0 && p_cur[1] > 0) {
            cv::circle(img2_show, cv::Point2f(p_cur[0], p_cur[1]), 2, cv::Scalar(0, 250, 0), 2);
            cv::line(img2_show, cv::Point2f(p_ref[0], p_ref[1]), cv::Point2f(p_cur[0], p_cur[1]),
                     cv::Scalar(0, 250, 0));
        }
    }
    cv::imshow("current", img2_show);
    cv::waitKey();
}

void JacobianAccumulator::accumulate_jacobian(const cv::Range &range) {

    // parameters
    const int half_patch_size = 1;
    int cnt_good = 0;
    Matrix6d hessian = Matrix6d::Zero();
    Vector6d bias = Vector6d::Zero();
    double cost_tmp = 0;

    for (size_t i = range.start; i < range.end; i++) {

        // compute the projection in the second image
        Eigen::Vector3d point_ref =
            depth_ref[i] * Eigen::Vector3d((px_ref[i][0] - cx) / fx, (px_ref[i][1] - cy) / fy, 1);
        Eigen::Vector3d point_cur = T21 * point_ref;
        if (point_cur[2] < 0)   // depth invalid
            continue;

        float u = fx * point_cur[0] / point_cur[2] + cx, v = fy * point_cur[1] / point_cur[2] + cy;
        if (u < half_patch_size || u > img2.cols - half_patch_size || v < half_patch_size ||
            v > img2.rows - half_patch_size)
            continue;

        projection[i] = Eigen::Vector2d(u, v);
        double X = point_cur[0], Y = point_cur[1], Z = point_cur[2],
            Z2 = Z * Z, Z_inv = 1.0 / Z, Z2_inv = Z_inv * Z_inv;
        cnt_good++;

        // and compute error and jacobian
        for (int x = -half_patch_size; x <= half_patch_size; x++)
            for (int y = -half_patch_size; y <= half_patch_size; y++) {

                double error = GetPixelValue(img1, px_ref[i][0] + x, px_ref[i][1] + y) -
                               GetPixelValue(img2, u + x, v + y);
                Matrix26d J_pixel_xi;
                Eigen::Vector2d J_img_pixel;

                J_pixel_xi(0, 0) = fx * Z_inv;
                J_pixel_xi(0, 1) = 0;
                J_pixel_xi(0, 2) = -fx * X * Z2_inv;
                J_pixel_xi(0, 3) = -fx * X * Y * Z2_inv;
                J_pixel_xi(0, 4) = fx + fx * X * X * Z2_inv;
                J_pixel_xi(0, 5) = -fx * Y * Z_inv;

                J_pixel_xi(1, 0) = 0;
                J_pixel_xi(1, 1) = fy * Z_inv;
                J_pixel_xi(1, 2) = -fy * Y * Z2_inv;
                J_pixel_xi(1, 3) = -fy - fy * Y * Y * Z2_inv;
                J_pixel_xi(1, 4) = fy * X * Y * Z2_inv;
                J_pixel_xi(1, 5) = fy * X * Z_inv;

                J_img_pixel = Eigen::Vector2d(
                    0.5 * (GetPixelValue(img2, u + 1 + x, v + y) - GetPixelValue(img2, u - 1 + x, v + y)),
                    0.5 * (GetPixelValue(img2, u + x, v + 1 + y) - GetPixelValue(img2, u + x, v - 1 + y))
                );

                // total jacobian
                Vector6d J = -1.0 * (J_img_pixel.transpose() * J_pixel_xi).transpose();

                hessian += J * J.transpose();
                bias += -error * J;
                cost_tmp += error * error;
            }
    }

    if (cnt_good) {
        // set hessian, bias and cost
        unique_lock lck(hessian_mutex);
        H += hessian;
        b += bias;
        cost += cost_tmp / cnt_good;
    }
}

void DirectPoseEstimationMultiLayer(
    const cv::Mat &img1,
    const cv::Mat &img2,
    const VecVector2d &px_ref,
    const vector depth_ref,
    Sophus::SE3d &T21) {

    // parameters
    int pyramids = 4;
    double pyramid_scale = 0.5;
    double scales[] = {1.0, 0.5, 0.25, 0.125};

    // create pyramids
    vector pyr1, pyr2; // image pyramids
    for (int i = 0; i < pyramids; i++) {
        if (i == 0) {
            pyr1.push_back(img1);
            pyr2.push_back(img2);
        } else {
            cv::Mat img1_pyr, img2_pyr;
            cv::resize(pyr1[i - 1], img1_pyr,
                       cv::Size(pyr1[i - 1].cols * pyramid_scale, pyr1[i - 1].rows * pyramid_scale));
            cv::resize(pyr2[i - 1], img2_pyr,
                       cv::Size(pyr2[i - 1].cols * pyramid_scale, pyr2[i - 1].rows * pyramid_scale));
            pyr1.push_back(img1_pyr);
            pyr2.push_back(img2_pyr);
        }
    }

    double fxG = fx, fyG = fy, cxG = cx, cyG = cy;  // backup the old values
    for (int level = pyramids - 1; level >= 0; level--) {
        VecVector2d px_ref_pyr; // set the keypoints in this pyramid level
        for (auto &px: px_ref) {
            px_ref_pyr.push_back(scales[level] * px);
        }

        // scale fx, fy, cx, cy in different pyramid levels
        fx = fxG * scales[level];
        fy = fyG * scales[level];
        cx = cxG * scales[level];
        cy = cyG * scales[level];
        DirectPoseEstimationSingleLayer(pyr1[level], pyr2[level], px_ref_pyr, depth_ref, T21);
    }

}

 

第九章和第十章：后端 1+2

编译示例，不会遇到问题。

第十一章：回环检测

需要先编译第三方功能包：DBoW3。

然后再编译时候，可能出错，信息如下：

ROS:~/SLAM/slambook2/ch11/build$ make
Scanning dependencies of target gen_vocab
[ 16%] Building CXX object CMakeFiles/gen_vocab.dir/gen_vocab_large.cpp.o
make[2]: *** No rule to make target '/usr/local/lib/libDBoW3.a', needed by 'gen_vocab'.  Stop.
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/gen_vocab.dir/all' failed
make[1]: *** [CMakeFiles/gen_vocab.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2

原因：

看一下~/slambook2/ch11/CMakeLists.txt，发现如下：

# dbow3 
# dbow3 is a simple lib so I assume you installed it in default directory 
set( DBoW3_INCLUDE_DIRS "/usr/local/include" )
set( DBoW3_LIBS "/usr/local/lib/libDBoW3.a" )

实际是：

ROS:~/SLAM/slambook2/3rdparty/DBoW3/build$ sudo make install
[sudo] password for relaybot: 
[ 60%] Built target DBoW3
[ 73%] Built target create_voc_step0
[ 86%] Built target demo_general
[100%] Built target create_voc_step1
Install the project...
-- Install configuration: "Release"
-- Installing: /usr/local/lib/cmake/FindDBoW3.cmake
-- Installing: /usr/local/lib/cmake/DBoW3/DBoW3Config.cmake
-- Installing: /usr/local/lib/libDBoW3.so.0.0.1
-- Installing: /usr/local/lib/libDBoW3.so.0.0
-- Installing: /usr/local/lib/libDBoW3.so
-- Installing: /usr/local/include/DBoW3/BowVector.h
-- Installing: /usr/local/include/DBoW3/DBoW3.h
-- Installing: /usr/local/include/DBoW3/Database.h
-- Installing: /usr/local/include/DBoW3/DescManip.h
-- Installing: /usr/local/include/DBoW3/FeatureVector.h
-- Installing: /usr/local/include/DBoW3/QueryResults.h
-- Installing: /usr/local/include/DBoW3/ScoringObject.h
-- Installing: /usr/local/include/DBoW3/Vocabulary.h
-- Installing: /usr/local/include/DBoW3/exports.h
-- Installing: /usr/local/include/DBoW3/quicklz.h
-- Installing: /usr/local/include/DBoW3/timers.h
-- Installing: /usr/local/bin/demo_general
-- Set runtime path of "/usr/local/bin/demo_general" to ""
-- Installing: /usr/local/bin/create_voc_step0
-- Set runtime path of "/usr/local/bin/create_voc_step0" to ""
-- Installing: /usr/local/bin/create_voc_step1
-- Set runtime path of "/usr/local/bin/create_voc_step1" to ""

将/usr/local/lib/libDBoW3.a改为/usr/local/lib/libDBoW3.so！！！

然后就一切ok。

第十二章：建图

正常编译，一切ok！

第十三章：实践：设计SLAM系统

需要先编译3rdparty/googletest，否则会报如下错误：

ROS:~/SLAM/slambook2/ch13/build$ cmake ..
-- The C compiler identification is GNU 7.4.0
-- The CXX compiler identification is GNU 7.4.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found OpenCV: /usr (found suitable version "3.2.0", minimum required is "3.1") 
-- Found Glog: /usr/include  
CMake Error at /usr/share/cmake-3.10/Modules/FindPackageHandleStandardArgs.cmake:137 (message):
  Could NOT find GTest (missing: GTEST_LIBRARY GTEST_MAIN_LIBRARY)
Call Stack (most recent call first):
  /usr/share/cmake-3.10/Modules/FindPackageHandleStandardArgs.cmake:378 (_FPHSA_FAILURE_MESSAGE)
  /usr/share/cmake-3.10/Modules/FindGTest.cmake:196 (FIND_PACKAGE_HANDLE_STANDARD_ARGS)
  CMakeLists.txt:38 (find_package)


-- Configuring incomplete, errors occurred!
See also "/home/relaybot/SLAM/slambook2/ch13/build/CMakeFiles/CMakeOutput.log"

安装好googletest，就一切正常了。

第十四章：SLAM：现在与未来

自学各种SLAM案例，推荐一个网址：OpenSLAM！

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附录A和附录B为数学基础，必须掌握

附录C～ROS入门：参考之前一篇博文如下：

• 沉迷机器人操作系统的一个理由和四种修仙秘籍

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这只是将全书案例在自己电脑上复现的过程，重点是：

SLAM理论和实践！！！

SLAM理论和实践！！！

SLAM理论和实践！！！

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每章具体备课内容，在开课前更新。

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