SLAM代碼之pose_estimation_3d3d

ICP中的使用SVD和使用BA的代碼如下：

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <Eigen/Core>
#include <Eigen/Dense>
#include <Eigen/Geometry>
#include <Eigen/SVD>
#include <g2o/core/base_vertex.h>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/optimization_algorithm_gauss_newton.h>
#include <g2o/core/optimization_algorithm_levenberg.h>
#include <g2o/solvers/dense/linear_solver_dense.h>
#include <chrono>
#include <sophus/se3.hpp>

using namespace std;
using namespace cv;

void find_feature_matches(
  const Mat &img_1, const Mat &img_2,
  std::vector<KeyPoint> &keypoints_1,
  std::vector<KeyPoint> &keypoints_2,
  std::vector<DMatch> &matches);

// 像素座標轉相機歸一化座標
Point2d pixel2cam(const Point2d &p, const Mat &K);

void pose_estimation_3d3d(
  const vector<Point3f> &pts1,
  const vector<Point3f> &pts2,
  Mat &R, Mat &t
);

void bundleAdjustment(
  const vector<Point3f> &points_3d,
  const vector<Point3f> &points_2d,
  Mat &R, Mat &t
);
///vertex and edges used in g2o ba
class VertexPose : public g2o::BaseVertex<6, Sophus::SE3d> {
public:
  EIGEN_MAKE_ALIGNED_OPERATOR_NEW;

  virtual void setToOriginImpl() override {
    _estimate = Sophus::SE3d();
  }

  /// left multiplication on SE3
  virtual void oplusImpl(const double *update) override {
    Eigen::Matrix<double, 6, 1> update_eigen;
    update_eigen << update[0], update[1], update[2], update[3], update[4], update[5];
    _estimate = Sophus::SE3d::exp(update_eigen) * _estimate;
  }

  virtual bool read(istream &in) override {}

  virtual bool write(ostream &out) const override {}
};


/// g2o edge
class EdgeProjectXYZRGBDPoseOnly:public g2o::BaseUnaryEdge<3,Eigen::Vector3d,VertexPose>{
public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;

    EdgeProjectXYZRGBDPoseOnly(const Eigen::Vector3d &point):_point(point){}

    virtual void computeError()override{
        const VertexPose *pose=static_cast<const VertexPose *>(_vertices[0]);
        _error=_measurement-pose->estimate()*_point;
    }
    virtual void linearizeOplus()override{
        VertexPose *pose=static_cast<VertexPose *>(_vertices[0]);
        Sophus::SE3d T=pose->estimate();
        Eigen::Vector3d xyz_trans=T * _point;
        _jacobianOplusXi.block<3,3>(0,0)=-Eigen::Matrix3d::Identity();
        _jacobianOplusXi.block<3,3>(0,3)=Sophus::SO3d::hat(xyz_trans);
    }
    bool read(istream &in){}

    bool write(ostream &out)const {}

protected:
    Eigen::Vector3d _point;
};
int main(int argc, char **argv) {
  if (argc != 5) {
    cout << "usage: pose_estimation_3d3d img1 img2 depth1 depth2" << endl;
    return 1;
  }
  //-- 讀取圖像
  Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);
  Mat img_2 = imread(argv[2], CV_LOAD_IMAGE_COLOR);

  vector<KeyPoint> keypoints_1, keypoints_2;
  vector<DMatch> matches;
  find_feature_matches(img_1, img_2, keypoints_1, keypoints_2, matches);
  cout << "一共找到了" << matches.size() << "組匹配點" << endl;

  // 建立3D點
  Mat depth1 = imread(argv[3], CV_LOAD_IMAGE_UNCHANGED);       // 深度圖爲16位無符號數，單通道圖像
  Mat depth2 = imread(argv[4], CV_LOAD_IMAGE_UNCHANGED);       // 深度圖爲16位無符號數，單通道圖像
  Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);
  vector<Point3f> pts1, pts2;

  for (DMatch m:matches) {
    ushort d1 = depth1.ptr<unsigned short>(int(keypoints_1[m.queryIdx].pt.y))[int(keypoints_1[m.queryIdx].pt.x)];
    ushort d2 = depth2.ptr<unsigned short>(int(keypoints_2[m.trainIdx].pt.y))[int(keypoints_2[m.trainIdx].pt.x)];
    if (d1 == 0 || d2 == 0)   // bad depth
      continue;
    Point2d p1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);
    Point2d p2 = pixel2cam(keypoints_2[m.trainIdx].pt, K);
    float dd1 = float(d1) / 5000.0;
    float dd2 = float(d2) / 5000.0;
    pts1.push_back(Point3f(p1.x * dd1, p1.y * dd1, dd1));
    pts2.push_back(Point3f(p2.x * dd2, p2.y * dd2, dd2));
  }

  cout << "3d-3d pairs: " << pts1.size() << endl;
  Mat R, t;
  pose_estimation_3d3d(pts1, pts2, R, t);
  cout << "ICP via SVD results: " << endl;
  cout << "R = " << R << endl;
  cout << "t = " << t << endl;
  cout << "R_inv = " << R.t() << endl;
  cout << "t_inv = " << -R.t() * t << endl;

  cout << "calling bundle adjustment" << endl;

  bundleAdjustment(pts1, pts2, R, t);

  // verify p1 = R * p2 + t
  for (int i = 0; i < 5; i++) {
    cout << "p1 = " << pts1[i] << endl;
    cout << "p2 = " << pts2[i] << endl;
    cout << "(R*p2+t) = " <<
         R * (Mat_<double>(3, 1) << pts2[i].x, pts2[i].y, pts2[i].z) + t
         << endl;
    cout << endl;
  }
}
void find_feature_matches(const Mat &img_1, const Mat &img_2,std::vector<KeyPoint> &keypoints_1,std::vector<KeyPoint> &keypoints_2,
std::vector<DMatch> &matches) {
    //初始化
    Mat descriptors_1,descriptors_2;
    Ptr<FeatureDetector>detector=ORB::create();
    Ptr<DescriptorExtractor>descriptor=ORB::create();
    Ptr<DescriptorMatcher>matcher=DescriptorMatcher::create("BruteForce-Hamming");
    //檢測Oriented FAST角點位置
    detector->detect(img_1,keypoints_1);
    detector->detect(img_2,keypoints_2);
    //計算描述子
    descriptor->compute(img_1,keypoints_1,descriptors_1);
    descriptor->compute(img_2,keypoints_2,descriptors_2);
    //對描述子進行匹配,使用Hamming距離
    vector<DMatch> match;
    matcher->match(descriptors_1,descriptors_2,match);
    //匹配點篩選
    //找出最大最小距離
    double min_dist=10000,max_dist=0;
    for(int i=0;i<descriptors_1.rows;i++){
        double dist=match[i].distance;
        if(dist<min_dist)min_dist=dist;
        if(dist>max_dist)max_dist=dist;
    }

    printf("-- Max dist : %f \n", max_dist);
    printf("-- Min dist : %f \n", min_dist);
    //找出誤匹配：匹配距離大於2倍的最小距離或者大於30
    for(int i=0;i<descriptors_1.rows;i++){
        if(match[i].distance<=max(2*min_dist,30.0)){
            matches.push_back(match[i]);
        }
    }
}
Point2d pixel2cam(const Point2d &p,const Mat &K){
    return Point2d(
        (p.x-K.at<double>(0,2))/K.at<double>(0,0),
        (p.y-K.at<double>(1,2))/K.at<double>(1,1)
    );
}

void pose_estimation_3d3d(const vector<Point3f>&pts1,const vector<Point3f>&pts2,Mat &R,Mat &t){
    Point3f p1,p2;
    int N=pts1.size();
    for(int i=0;i<N;i++){
        p1+=pts1[i];
        p2+=pts2[i];
    }
    //計算質心
    p1=Point3f(Vec3f(p1)/N);
    p2=Point3f(Vec3f(p2)/N);
    vector<Point3f>q1(N),q2(N);
    //去質心
    for(int i=0;i<N;i++){
        q1[i]=pts1[i]-p1;
        q2[i]=pts2[i]-p2;
    }
    Eigen::Matrix3d W = Eigen::Matrix3d::Zero();
    for(int i=0;i<N;i++){
        W+=Eigen::Vector3d(q1[i].x,q1[i].y,q1[i].z)*Eigen::Vector3d(q2[i].x,q2[i].y,q2[i].z).transpose();
    }
    cout << "W=" << W << endl;

    // SVD on W
    Eigen::JacobiSVD<Eigen::Matrix3d>svd(W,Eigen::ComputeFullU|Eigen::ComputeFullV);
    Eigen::Matrix3d U=svd.matrixU();
    Eigen::Matrix3d V=svd.matrixV();

    cout << "U=" << U << endl;
    cout << "V=" << V << endl;
    //計算R=UV^T
    Eigen::Matrix3d R_ = U * (V.transpose());
    if (R_.determinant() < 0) {
        R_ = -R_;
    }
    //t*=p-Rp'
    Eigen::Vector3d t_ = Eigen::Vector3d(p1.x, p1.y, p1.z) - R_ * Eigen::Vector3d(p2.x, p2.y, p2.z);

    // convert to cv::Mat
    R = (Mat_<double>(3, 3) <<
        R_(0, 0), R_(0, 1), R_(0, 2),
        R_(1, 0), R_(1, 1), R_(1, 2),
        R_(2, 0), R_(2, 1), R_(2, 2)
    );
    t = (Mat_<double>(3, 1) << t_(0, 0), t_(1, 0), t_(2, 0));
}

void bundleAdjustment(const vector<Point3f> &pts1,const vector<Point3f> &pts2,Mat&R,Mat &t){
    //構建圖優化，先設定g2o
    typedef g2o::BlockSolverX BlockSolverType;
    typedef g2o::LinearSolverDense<BlockSolverType::PoseMatrixType>LinearSolverType;///線性求解器

    //梯度下降方法，可以從GN,LM,DogLeg中選
    auto solver=new g2o::OptimizationAlgorithmLevenberg(g2o::make_unique<BlockSolverType>(g2o::make_unique<LinearSolverType>()));
    g2o::SparseOptimizer optimizer;     //圖模型
    optimizer.setAlgorithm(solver);     //設置求解器
    optimizer.setVerbose(true);         //打開調試輸出

    //vertex
    VertexPose *pose=new VertexPose();//camera pose
    pose->setId(0);
    pose->setEstimate(Sophus::SE3d());
    optimizer.addVertex(pose);

    //edges
    for(size_t i=0;i<pts1.size();i++){
        EdgeProjectXYZRGBDPoseOnly *edge=new EdgeProjectXYZRGBDPoseOnly(Eigen::Vector3d(pts2[i].x,pts2[i].y,pts2[i].z));
        edge->setVertex(0,pose);
        edge->setMeasurement(Eigen::Vector3d(pts1[i].x,pts1[i].y,pts1[i].z));
        edge->setInformation(Eigen::Matrix3d::Identity());
        optimizer.addEdge(edge);
    }

    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
    optimizer.initializeOptimization();
    optimizer.optimize(10);
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
    cout << "optimization costs time: " << time_used.count() << " seconds." << endl;

    cout << endl << "after optimization:" << endl;
    cout<<"T=\n"<<pose->estimate().matrix()<<endl;

    //convert to cv::Mat
    Eigen::Matrix3d R_=pose->estimate().rotationMatrix();
    Eigen::Vector3d t_=pose->estimate().translation();
    R=(Mat_<double>(3,3)<<
    R_(0,0),R_(0,1),R_(0,2),
    R_(1,0),R_(1,1),R_(1,2),
    R_(2,0),R_(2,1),R_(2,2));
    t=(Mat_<double>(3,1)<<t_(0,0),t_(1,0),t_(2,0));


}

CMakeLists.txt

• 出現fmt未引用，增加fm
• 出現/usr/bin/ld: cannot find -lopencv_xxx，改爲find_package(OpenCV 3 REQUIRED)

cmake_minimum_required(VERSION 2.8)
project(ch7)
set(CMAKE_BUILD_TYPE Release)
set(CMAKE_CXX_FLAGS "-std=c++14 -O2 ${SSE_FLAGS} -msse4") 

list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
list(APPEND CMAKE_MODULE_PATH /home/xxx/MyLibs/g2o-master/cmake_modules)
set(G2O_ROOT/usr/local/include/g2o)
set(G2O_LIBS/usr/local/include/g2o)

include_directories("/usr/include/eigen3")
#OpenCV
find_package(OpenCV 3 REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
#Sophus
find_package(Sophus REQUIRED)
include_directories(${Sophus_INCLUDE_DIRS})
#g2o
find_package(G2O REQUIRED)
include_directories(${G2O_INCLUDE_DIRS})

add_executable(pose_estimation_3d3d pose_estimation_3d3d.cpp)
target_link_libraries(pose_estimation_3d3d
        g2o_core g2o_stuff
        ${OpenCV_LIBS} fmt)