用g2o自带的顶点和边进行SLAM优化
例如：重写一下slam14讲pose_estimation_3d2d
这里写目录标题
一、使用 g2o::VertexSE3Expmap和一元边EdgeSE3ProjectXYZOnlyPose
1.引入<g2o/types/sba/types_six_dof_expmap.h>
代码如下：
#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 <g2o/core/base_vertex.h>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/sparse_optimizer.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/solver.h>
#include <g2o/core/optimization_algorithm_gauss_newton.h>
#include <g2o/solvers/dense/linear_solver_dense.h>
#include <g2o/types/sba/types_six_dof_expmap.h>
#include <sophus/se3.hpp>
#include <chrono>
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);
// 像素坐标转相机归一化坐标
Point2d pixel2cam(const Point2d &p, const Mat &K);
// BA by g2o
typedef vector<Eigen::Vector2d, Eigen::aligned_allocator<Eigen::Vector2d>> VecVector2d;
typedef vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>> VecVector3d;
void bundleAdjustmentG2O(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose
);
// BA by gauss-newton
void bundleAdjustmentGaussNewton(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose
);
int main(int argc, char **argv) {
if (argc != 5) {
cout << "usage: pose_estimation_3d2d img1 img2 depth1 depth2" << endl;
return 1;
}
//-- 读取图像
Mat img_1 = imread(argv[1], cv::IMREAD_COLOR);
Mat img_2 = imread(argv[2], cv::IMREAD_COLOR);
assert(img_1.data && img_2.data && "Can not load images!");
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() << "组匹配点" << endl;
// 建立3D点
Mat d1 = imread(argv[3], cv::IMREAD_UNCHANGED); // 深度图为16位无符号数，单通道图像
Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);
vector<Point3f> pts_3d;
vector<Point2f> pts_2d;
//针对匹配成功的点对，读取对应点img1的深度值
for (DMatch m:matches) {
ushort d = d1.ptr<unsigned short>(int(keypoints_1[m.queryIdx].pt.y))[int(keypoints_1[m.queryIdx].pt.x)];
if (d == 0) // bad depth
continue;
float dd = d / 5000.0;
Point2d p1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);
pts_3d.push_back(Point3f(p1.x * dd, p1.y * dd, dd));
pts_2d.push_back(keypoints_2[m.trainIdx].pt);
}
cout << "3d-2d pairs: " << pts_3d.size() << endl;
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
Mat r, t;
solvePnP(pts_3d, pts_2d, K, Mat(), r, t, false); // 调用OpenCV 的 PnP 求解，可选择EPNP，DLS等方法
Mat R;
cv::Rodrigues(r, R); // r为旋转向量形式，用Rodrigues公式转换为矩阵
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve pnp in opencv cost time: " << time_used.count() << " seconds." << endl;
cout << "R=" << endl << R << endl;
cout << "t=" << endl << t << endl;
VecVector3d pts_3d_eigen;
VecVector2d pts_2d_eigen;
for (size_t i = 0; i < pts_3d.size(); ++i) {
pts_3d_eigen.push_back(Eigen::Vector3d(pts_3d[i].x, pts_3d[i].y, pts_3d[i].z));
pts_2d_eigen.push_back(Eigen::Vector2d(pts_2d[i].x, pts_2d[i].y));
}
cout << "calling bundle adjustment by gauss newton" << endl;
Sophus::SE3d pose_gn;
t1 = chrono::steady_clock::now();
bundleAdjustmentGaussNewton(pts_3d_eigen, pts_2d_eigen, K, pose_gn);
t2 = chrono::steady_clock::now();
time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve pnp by gauss newton cost time: " << time_used.count() << " seconds." << endl;
cout << "calling bundle adjustment by g2o" << endl;
Sophus::SE3d pose_g2o;
t1 = chrono::steady_clock::now();
bundleAdjustmentG2O(pts_3d_eigen, pts_2d_eigen, K, pose_g2o);
t2 = chrono::steady_clock::now();
time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve pnp by g2o cost time: " << time_used.count() << " seconds." << endl;
return 0;
}
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;
// used in OpenCV3
Ptr<FeatureDetector> detector = ORB::create();
Ptr<DescriptorExtractor> descriptor = ORB::create();
// use this if you are in OpenCV2
// Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
// Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
//-- 第一步:检测 Oriented FAST 角点位置
detector->detect(img_1, keypoints_1);
detector->detect(img_2, keypoints_2);
//-- 第二步:根据角点位置计算 BRIEF 描述子
descriptor->compute(img_1, keypoints_1, descriptors_1);
descriptor->compute(img_2, keypoints_2, descriptors_2);
//-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
vector<DMatch> match;
// BFMatcher matcher ( NORM_HAMMING );
matcher->match(descriptors_1, descriptors_2, match);
//-- 第四步:匹配点对筛选
double min_dist = 10000, max_dist = 0;
//找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
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);
//当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
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 bundleAdjustmentGaussNewton(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose) {
typedef Eigen::Matrix<double, 6, 1> Vector6d;
const int iterations = 10;
double cost = 0, lastCost = 0;
double fx = K.at<double>(0, 0);
double fy = K.at<double>(1, 1);
double cx = K.at<double>(0, 2);
double cy = K.at<double>(1, 2);
for (int iter = 0; iter < iterations; iter++) {
Eigen::Matrix<double, 6, 6> H = Eigen::Matrix<double, 6, 6>::Zero();
Vector6d b = Vector6d::Zero();
cost = 0;
// compute cost
for (int i = 0; i < points_3d.size(); i++) {
Eigen::Vector3d pc = pose * points_3d[i];
double inv_z = 1.0 / pc[2];
double inv_z2 = inv_z * inv_z;
Eigen::Vector2d proj(fx * pc[0] / pc[2] + cx, fy * pc[1] / pc[2] + cy);
Eigen::Vector2d e = points_2d[i] - proj;
cost += e.squaredNorm();
Eigen::Matrix<double, 2, 6> J;
J << -fx * inv_z,
0,
fx * pc[0] * inv_z2,
fx * pc[0] * pc[1] * inv_z2,
-fx - fx * pc[0] * pc[0] * inv_z2,
fx * pc[1] * inv_z,
0,
-fy * inv_z,
fy * pc[1] * inv_z2,
fy + fy * pc[1] * pc[1] * inv_z2,
-fy * pc[0] * pc[1] * inv_z2,
-fy * pc[0] * inv_z;
H += J.transpose() * J;
b += -J.transpose() * e;
}
Vector6d dx;
dx = H.ldlt().solve(b);
if (isnan(dx[0])) {
cout << "result is nan!" << endl;
break;
}
if (iter > 0 && cost >= lastCost) {
// cost increase, update is not good
cout << "cost: " << cost << ", last cost: " << lastCost << endl;
break;
}
// update your estimation
pose = Sophus::SE3d::exp(dx) * pose;
lastCost = cost;
cout << "iteration " << iter << " cost=" << std::setprecision(12) << cost << endl;
if (dx.norm() < 1e-6) {
// converge
break;
}
}
cout << "pose by g-n: \n" << pose.matrix() << endl;
}
//
/ 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 { return true; }
//
// virtual bool write(ostream &out) const override { return true; }
//};
//
//class EdgeProjection : public g2o::BaseUnaryEdge<2, Eigen::Vector2d, VertexPose> {
//public:
// EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
//
// EdgeProjection(const Eigen::Vector3d &pos, const Eigen::Matrix3d &K) : _pos3d(pos), _K(K) {}
//
// virtual void computeError() override {
// const VertexPose *v = static_cast<VertexPose *> (_vertices[0]);
// Sophus::SE3d T = v->estimate();
// Eigen::Vector3d pos_pixel = _K * (T * _pos3d);
// pos_pixel /= pos_pixel[2];
// _error = _measurement - pos_pixel.head<2>();
// }
//
// virtual void linearizeOplus() override {
// const VertexPose *v = static_cast<VertexPose *> (_vertices[0]);
// Sophus::SE3d T = v->estimate();
// Eigen::Vector3d pos_cam = T * _pos3d;
// double fx = _K(0, 0);
// double fy = _K(1, 1);
// double cx = _K(0, 2);
// double cy = _K(1, 2);
// double X = pos_cam[0];
// double Y = pos_cam[1];
// double Z = pos_cam[2];
// double Z2 = Z * Z;
// _jacobianOplusXi
// << -fx / Z, 0, fx * X / Z2, fx * X * Y / Z2, -fx - fx * X * X / Z2, fx * Y / Z,
// 0, -fy / Z, fy * Y / (Z * Z), fy + fy * Y * Y / Z2, -fy * X * Y / Z2, -fy * X / Z;
// }
//
// virtual bool read(istream &in) override { return true; }
//
// virtual bool write(ostream& out) const override { return true; }
//
//private:
// Eigen::Vector3d _pos3d;
// Eigen::Matrix3d _K;
//};
void bundleAdjustmentG2O(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose) {
//g2o::BlockSolver_6_3;
// 构建图优化，先设定g2o
typedef g2o::BlockSolver<g2o::BlockSolverTraits<6, 3>> BlockSolverType; // pose is 6, landmark is 3
typedef g2o::LinearSolverDense<BlockSolverType::PoseMatrixType> LinearSolverType; // 线性求解器类型
// 梯度下降方法，可以从GN, LM, DogLeg 中选
auto solver = new g2o::OptimizationAlgorithmGaussNewton(
g2o::make_unique<BlockSolverType>(g2o::make_unique<LinearSolverType>()));
g2o::SparseOptimizer optimizer; // 图模型
optimizer.setAlgorithm(solver); // 设置求解器
optimizer.setVerbose(true); // 打开调试输出
//这里不用SLAM14讲里自定义的顶点和边了
//g2o::VertexSE3Expmap;
//g2o::EdgeSE3ProjectXYZOnlyPose;
// vertex
g2o::VertexSE3Expmap*vertex_pose = new g2o::VertexSE3Expmap(); // camera vertex_pose
vertex_pose->setId(0);
vertex_pose->setEstimate(g2o::SE3Quat());
vertex_pose->setFixed(false);
optimizer.addVertex(vertex_pose);
// K
Eigen::Matrix3d K_eigen;
K_eigen <<
K.at<double>(0, 0), K.at<double>(0, 1), K.at<double>(0, 2),
K.at<double>(1, 0), K.at<double>(1, 1), K.at<double>(1, 2),
K.at<double>(2, 0), K.at<double>(2, 1), K.at<double>(2, 2);
// edges
int index = 1;
for (size_t i = 0; i < points_2d.size(); ++i) {
auto p2d = points_2d[i];
auto p3d = points_3d[i];
g2o::EdgeSE3ProjectXYZOnlyPose* edge = new g2o::EdgeSE3ProjectXYZOnlyPose();
edge->setId(index);
edge->setVertex(0, vertex_pose);
edge->setMeasurement(p2d);
edge->setInformation(Eigen::Matrix2d::Identity());
edge->fx = K.at<double>(0, 0);
edge->fy = K.at<double>(1, 1);
edge->cx = K.at<double>(0, 2);
edge->cy = K.at<double>(1, 2);
edge->Xw = p3d;
optimizer.addEdge(edge);
index++;
//auto p2d = points_2d[i];
//auto p3d = points_3d[i];
//EdgeProjection *edge = new EdgeProjection(p3d, K_eigen);
//edge->setId(index);
//edge->setVertex(0, vertex_pose);
//edge->setMeasurement(p2d);
//edge->setInformation(Eigen::Matrix2d::Identity());
//optimizer.addEdge(edge);
//index++;
}
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
optimizer.setVerbose(true);
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 << "pose estimated by g2o =\n" << vertex_pose->estimate().to_homogeneous_matrix() << endl;
//pose = vertex_pose->estimate();
}
二、.利用 g2o::VertexSE3Expmap相机位姿；g2o::VertexSBAPointXYZ路标位置，二元边g2o::EdgeSE3ProjectXYZ；只是我这里困惑的是为什么非要把
mappoint->setFixed(true);
如果不把路标设置为定值，就会运行出错，有人知道吗？ ，有时就会出现下图一样，误差为0了，也就是优化的过分好，以至于忽略了真实的相机位姿了，不过有时也会出现极大值，就是chi2= nan跑飞了，所以这里要setfixed
代码如下（示例）：
#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 <g2o/core/base_vertex.h>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/sparse_optimizer.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/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<g2o/core/robust_kernel.h>
#include<g2o/core/robust_kernel_impl.h>
#include <g2o/types/sba/types_six_dof_expmap.h>
#include <sophus/se3.hpp>
#include <chrono>
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);
// 像素坐标转相机归一化坐标
Point2d pixel2cam(const Point2d &p, const Mat &K);
// BA by g2o
typedef vector<Eigen::Vector2d, Eigen::aligned_allocator<Eigen::Vector2d>> VecVector2d;
typedef vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>> VecVector3d;
void bundleAdjustmentG2O(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose
);
// BA by gauss-newton
void bundleAdjustmentGaussNewton(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose
);
int main(int argc, char **argv) {
if (argc != 5) {
cout << "usage: pose_estimation_3d2d img1 img2 depth1 depth2" << endl;
return 1;
}
//-- 读取图像
Mat img_1 = imread(argv[1], cv::IMREAD_COLOR);
Mat img_2 = imread(argv[2], cv::IMREAD_COLOR);
assert(img_1.data && img_2.data && "Can not load images!");
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() << "组匹配点" << endl;
// 建立3D点
Mat d1 = imread(argv[3], cv::IMREAD_UNCHANGED); // 深度图为16位无符号数，单通道图像
Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);
vector<Point3f> pts_3d;
vector<Point2f> pts_2d;
//针对匹配成功的点对，读取对应点img1的深度值
for (DMatch m:matches) {
ushort d = d1.ptr<unsigned short>(int(keypoints_1[m.queryIdx].pt.y))[int(keypoints_1[m.queryIdx].pt.x)];
if (d == 0) // bad depth
continue;
float dd = d / 5000.0;
Point2d p1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);
pts_3d.push_back(Point3f(p1.x * dd, p1.y * dd, dd));
pts_2d.push_back(keypoints_2[m.trainIdx].pt);
}
cout << "3d-2d pairs: " << pts_3d.size() << endl;
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
Mat r, t;
solvePnP(pts_3d, pts_2d, K, Mat(), r, t, false); // 调用OpenCV 的 PnP 求解，可选择EPNP，DLS等方法
Mat R;
cv::Rodrigues(r, R); // r为旋转向量形式，用Rodrigues公式转换为矩阵
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve pnp in opencv cost time: " << time_used.count() << " seconds." << endl;
cout << "R=" << endl << R << endl;
cout << "t=" << endl << t << endl;
VecVector3d pts_3d_eigen;
VecVector2d pts_2d_eigen;
for (size_t i = 0; i < pts_3d.size(); ++i) {
pts_3d_eigen.push_back(Eigen::Vector3d(pts_3d[i].x, pts_3d[i].y, pts_3d[i].z));
pts_2d_eigen.push_back(Eigen::Vector2d(pts_2d[i].x, pts_2d[i].y));
}
cout << "calling bundle adjustment by gauss newton" << endl;
Sophus::SE3d pose_gn;
t1 = chrono::steady_clock::now();
bundleAdjustmentGaussNewton(pts_3d_eigen, pts_2d_eigen, K, pose_gn);
t2 = chrono::steady_clock::now();
time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve pnp by gauss newton cost time: " << time_used.count() << " seconds." << endl;
cout << "calling bundle adjustment by g2o" << endl;
Sophus::SE3d pose_g2o;
t1 = chrono::steady_clock::now();
bundleAdjustmentG2O(pts_3d_eigen, pts_2d_eigen, K, pose_g2o);
t2 = chrono::steady_clock::now();
time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve pnp by g2o cost time: " << time_used.count() << " seconds." << endl;
return 0;
}
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;
// used in OpenCV3
Ptr<FeatureDetector> detector = ORB::create();
Ptr<DescriptorExtractor> descriptor = ORB::create();
// use this if you are in OpenCV2
// Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
// Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
//-- 第一步:检测 Oriented FAST 角点位置
detector->detect(img_1, keypoints_1);
detector->detect(img_2, keypoints_2);
//-- 第二步:根据角点位置计算 BRIEF 描述子
descriptor->compute(img_1, keypoints_1, descriptors_1);
descriptor->compute(img_2, keypoints_2, descriptors_2);
//-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
vector<DMatch> match;
// BFMatcher matcher ( NORM_HAMMING );
matcher->match(descriptors_1, descriptors_2, match);
//-- 第四步:匹配点对筛选
double min_dist = 10000, max_dist = 0;
//找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
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);
//当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
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 bundleAdjustmentGaussNewton(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose) {
typedef Eigen::Matrix<double, 6, 1> Vector6d;
const int iterations = 10;
double cost = 0, lastCost = 0;
double fx = K.at<double>(0, 0);
double fy = K.at<double>(1, 1);
double cx = K.at<double>(0, 2);
double cy = K.at<double>(1, 2);
for (int iter = 0; iter < iterations; iter++) {
Eigen::Matrix<double, 6, 6> H = Eigen::Matrix<double, 6, 6>::Zero();
Vector6d b = Vector6d::Zero();
cost = 0;
// compute cost
for (int i = 0; i < points_3d.size(); i++) {
Eigen::Vector3d pc = pose * points_3d[i];
double inv_z = 1.0 / pc[2];
double inv_z2 = inv_z * inv_z;
Eigen::Vector2d proj(fx * pc[0] / pc[2] + cx, fy * pc[1] / pc[2] + cy);
Eigen::Vector2d e = points_2d[i] - proj;
cost += e.squaredNorm();
Eigen::Matrix<double, 2, 6> J;
J << -fx * inv_z,
0,
fx * pc[0] * inv_z2,
fx * pc[0] * pc[1] * inv_z2,
-fx - fx * pc[0] * pc[0] * inv_z2,
fx * pc[1] * inv_z,
0,
-fy * inv_z,
fy * pc[1] * inv_z2,
fy + fy * pc[1] * pc[1] * inv_z2,
-fy * pc[0] * pc[1] * inv_z2,
-fy * pc[0] * inv_z;
H += J.transpose() * J;
b += -J.transpose() * e;
}
Vector6d dx;
dx = H.ldlt().solve(b);
if (isnan(dx[0])) {
cout << "result is nan!" << endl;
break;
}
if (iter > 0 && cost >= lastCost) {
// cost increase, update is not good
cout << "cost: " << cost << ", last cost: " << lastCost << endl;
break;
}
// update your estimation
pose = Sophus::SE3d::exp(dx) * pose;
lastCost = cost;
cout << "iteration " << iter << " cost=" << std::setprecision(12) << cost << endl;
if (dx.norm() < 1e-6) {
// converge
break;
}
}
cout << "pose by g-n: \n" << pose.matrix() << endl;
}
//
/ 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 { return true; }
//
// virtual bool write(ostream &out) const override { return true; }
//};
//
//class EdgeProjection : public g2o::BaseUnaryEdge<2, Eigen::Vector2d, VertexPose> {
//public:
// EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
//
// EdgeProjection(const Eigen::Vector3d &pos, const Eigen::Matrix3d &K) : _pos3d(pos), _K(K) {}
//
// virtual void computeError() override {
// const VertexPose *v = static_cast<VertexPose *> (_vertices[0]);
// Sophus::SE3d T = v->estimate();
// Eigen::Vector3d pos_pixel = _K * (T * _pos3d);
// pos_pixel /= pos_pixel[2];
// _error = _measurement - pos_pixel.head<2>();
// }
//
// virtual void linearizeOplus() override {
// const VertexPose *v = static_cast<VertexPose *> (_vertices[0]);
// Sophus::SE3d T = v->estimate();
// Eigen::Vector3d pos_cam = T * _pos3d;
// double fx = _K(0, 0);
// double fy = _K(1, 1);
// double cx = _K(0, 2);
// double cy = _K(1, 2);
// double X = pos_cam[0];
// double Y = pos_cam[1];
// double Z = pos_cam[2];
// double Z2 = Z * Z;
// _jacobianOplusXi
// << -fx / Z, 0, fx * X / Z2, fx * X * Y / Z2, -fx - fx * X * X / Z2, fx * Y / Z,
// 0, -fy / Z, fy * Y / (Z * Z), fy + fy * Y * Y / Z2, -fy * X * Y / Z2, -fy * X / Z;
// }
//
// virtual bool read(istream &in) override { return true; }
//
// virtual bool write(ostream& out) const override { return true; }
//
//private:
// Eigen::Vector3d _pos3d;
// Eigen::Matrix3d _K;
//};
void bundleAdjustmentG2O(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose) {
//g2o::BlockSolver_6_3;
// 构建图优化，先设定g2o
typedef g2o::BlockSolver<g2o::BlockSolverTraits<6, 3>> BlockSolverType; // pose is 6, landmark is 3
typedef g2o::LinearSolverDense<BlockSolverType::PoseMatrixType> LinearSolverType; // 线性求解器类型
// 梯度下降方法，可以从GN, LM, DogLeg 中选
auto solver = new g2o::OptimizationAlgorithmLevenberg(
g2o::make_unique<BlockSolverType>(g2o::make_unique<LinearSolverType>()));
g2o::SparseOptimizer optimizer; // 图模型
optimizer.setAlgorithm(solver); // 设置求解器
optimizer.setVerbose(true); // 打开调试输出
//这里不用SLAM14讲里自定义的顶点和边了
//g2o::VertexSE3Expmap;
//g2o::EdgeSE3ProjectXYZOnlyPose;
// vertex
g2o::VertexSE3Expmap*vertex_pose = new g2o::VertexSE3Expmap(); // camera vertex_pose
vertex_pose->setId(0);
vertex_pose->setEstimate(g2o::SE3Quat());
vertex_pose->setFixed(false);
optimizer.addVertex(vertex_pose);
// K
Eigen::Matrix3d K_eigen;
K_eigen <<
K.at<double>(0, 0), K.at<double>(0, 1), K.at<double>(0, 2),
K.at<double>(1, 0), K.at<double>(1, 1), K.at<double>(1, 2),
K.at<double>(2, 0), K.at<double>(2, 1), K.at<double>(2, 2);
// edges
int index = 1;
for (size_t i = 0; i < points_2d.size(); ++i) {
auto p2d = points_2d[i];
auto p3d = points_3d[i];
g2o::VertexSBAPointXYZ* mappoint = new g2o::VertexSBAPointXYZ();
//因为id为0的是相机顶点
mappoint->setId(i + 1);
mappoint->setEstimate(p3d);
mappoint->setMarginalized(true);
//一直不明白为什么要setfixed
mappoint->setFixed(true);
optimizer.addVertex(mappoint);
g2o::EdgeSE3ProjectXYZ* edge = new g2o::EdgeSE3ProjectXYZ();
edge->setVertex(0, mappoint);
edge->setVertex(1, vertex_pose);
edge->setMeasurement(p2d);
edge->setInformation(Eigen::Matrix2d::Identity());
edge->setParameterId(0, 0);
edge->setRobustKernel(new g2o::RobustKernelHuber());
edge->fx = K.at<double>(0, 0);
edge->fy = K.at<double>(1, 1);
edge->cx = K.at<double>(0, 2);
edge->cy = K.at<double>(1, 2);
optimizer.addEdge(edge);
//g2o::EdgeSE3ProjectXYZOnlyPose* edge = new g2o::EdgeSE3ProjectXYZOnlyPose();
//edge->setId(index);
//edge->setVertex(0, vertex_pose);
//edge->setMeasurement(p2d);
//edge->setInformation(Eigen::Matrix2d::Identity());
//edge->fx = K.at<double>(0, 0);
//edge->fy = K.at<double>(1, 1);
//edge->cx = K.at<double>(0, 2);
//edge->cy = K.at<double>(1, 2);
//edge->Xw = p3d;
//optimizer.addEdge(edge);
//index++;
//auto p2d = points_2d[i];
//auto p3d = points_3d[i];
//EdgeProjection *edge = new EdgeProjection(p3d, K_eigen);
//edge->setId(index);
//edge->setVertex(0, vertex_pose);
//edge->setMeasurement(p2d);
//edge->setInformation(Eigen::Matrix2d::Identity());
//optimizer.addEdge(edge);
//index++;
}
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
optimizer.setVerbose(true);
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 << "pose estimated by g2o =\n" << vertex_pose->estimate().to_homogeneous_matrix() << endl;
//pose = vertex_pose->estimate();
}
三、利用 g2o::VertexSE3Expmap相机位姿、g2o::VertexSBAPointXYZ路标位置、g2o::EdgeProjectXYZ2UV二元边；
参考文章：https://github.com/SuYouge/g2o-example/blob/master/slam14-7/pnp_g2o_defined.cpp
#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 <g2o/core/base_vertex.h>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/sparse_optimizer.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/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<g2o/core/robust_kernel.h>
#include<g2o/core/robust_kernel_impl.h>
#include <g2o/types/sba/types_six_dof_expmap.h>
#include <sophus/se3.hpp>
#include <chrono>
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);
// 像素坐标转相机归一化坐标
Point2d pixel2cam(const Point2d &p, const Mat &K);
// BA by g2o
typedef vector<Eigen::Vector2d, Eigen::aligned_allocator<Eigen::Vector2d>> VecVector2d;
typedef vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>> VecVector3d;
void bundleAdjustmentG2O(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose
);
// BA by gauss-newton
void bundleAdjustmentGaussNewton(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose
);
int main(int argc, char **argv) {
if (argc != 5) {
cout << "usage: pose_estimation_3d2d img1 img2 depth1 depth2" << endl;
return 1;
}
//-- 读取图像
Mat img_1 = imread(argv[1], cv::IMREAD_COLOR);
Mat img_2 = imread(argv[2], cv::IMREAD_COLOR);
assert(img_1.data && img_2.data && "Can not load images!");
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() << "组匹配点" << endl;
// 建立3D点
Mat d1 = imread(argv[3], cv::IMREAD_UNCHANGED); // 深度图为16位无符号数，单通道图像
Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);
vector<Point3f> pts_3d;
vector<Point2f> pts_2d;
//针对匹配成功的点对，读取对应点img1的深度值
for (DMatch m:matches) {
ushort d = d1.ptr<unsigned short>(int(keypoints_1[m.queryIdx].pt.y))[int(keypoints_1[m.queryIdx].pt.x)];
if (d == 0) // bad depth
continue;
float dd = d / 5000.0;
Point2d p1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);
pts_3d.push_back(Point3f(p1.x * dd, p1.y * dd, dd));
pts_2d.push_back(keypoints_2[m.trainIdx].pt);
}
cout << "3d-2d pairs: " << pts_3d.size() << endl;
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
Mat r, t;
solvePnP(pts_3d, pts_2d, K, Mat(), r, t, false); // 调用OpenCV 的 PnP 求解，可选择EPNP，DLS等方法
Mat R;
cv::Rodrigues(r, R); // r为旋转向量形式，用Rodrigues公式转换为矩阵
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve pnp in opencv cost time: " << time_used.count() << " seconds." << endl;
cout << "R=" << endl << R << endl;
cout << "t=" << endl << t << endl;
VecVector3d pts_3d_eigen;
VecVector2d pts_2d_eigen;
for (size_t i = 0; i < pts_3d.size(); ++i) {
pts_3d_eigen.push_back(Eigen::Vector3d(pts_3d[i].x, pts_3d[i].y, pts_3d[i].z));
pts_2d_eigen.push_back(Eigen::Vector2d(pts_2d[i].x, pts_2d[i].y));
}
cout << "calling bundle adjustment by gauss newton" << endl;
Sophus::SE3d pose_gn;
t1 = chrono::steady_clock::now();
bundleAdjustmentGaussNewton(pts_3d_eigen, pts_2d_eigen, K, pose_gn);
t2 = chrono::steady_clock::now();
time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve pnp by gauss newton cost time: " << time_used.count() << " seconds." << endl;
cout << "calling bundle adjustment by g2o" << endl;
Sophus::SE3d pose_g2o;
t1 = chrono::steady_clock::now();
bundleAdjustmentG2O(pts_3d_eigen, pts_2d_eigen, K, pose_g2o);
t2 = chrono::steady_clock::now();
time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve pnp by g2o cost time: " << time_used.count() << " seconds." << endl;
return 0;
}
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;
// used in OpenCV3
Ptr<FeatureDetector> detector = ORB::create();
Ptr<DescriptorExtractor> descriptor = ORB::create();
// use this if you are in OpenCV2
// Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
// Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
//-- 第一步:检测 Oriented FAST 角点位置
detector->detect(img_1, keypoints_1);
detector->detect(img_2, keypoints_2);
//-- 第二步:根据角点位置计算 BRIEF 描述子
descriptor->compute(img_1, keypoints_1, descriptors_1);
descriptor->compute(img_2, keypoints_2, descriptors_2);
//-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
vector<DMatch> match;
// BFMatcher matcher ( NORM_HAMMING );
matcher->match(descriptors_1, descriptors_2, match);
//-- 第四步:匹配点对筛选
double min_dist = 10000, max_dist = 0;
//找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
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);
//当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
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 bundleAdjustmentGaussNewton(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose) {
typedef Eigen::Matrix<double, 6, 1> Vector6d;
const int iterations = 10;
double cost = 0, lastCost = 0;
double fx = K.at<double>(0, 0);
double fy = K.at<double>(1, 1);
double cx = K.at<double>(0, 2);
double cy = K.at<double>(1, 2);
for (int iter = 0; iter < iterations; iter++) {
Eigen::Matrix<double, 6, 6> H = Eigen::Matrix<double, 6, 6>::Zero();
Vector6d b = Vector6d::Zero();
cost = 0;
// compute cost
for (int i = 0; i < points_3d.size(); i++) {
Eigen::Vector3d pc = pose * points_3d[i];
double inv_z = 1.0 / pc[2];
double inv_z2 = inv_z * inv_z;
Eigen::Vector2d proj(fx * pc[0] / pc[2] + cx, fy * pc[1] / pc[2] + cy);
Eigen::Vector2d e = points_2d[i] - proj;
cost += e.squaredNorm();
Eigen::Matrix<double, 2, 6> J;
J << -fx * inv_z,
0,
fx * pc[0] * inv_z2,
fx * pc[0] * pc[1] * inv_z2,
-fx - fx * pc[0] * pc[0] * inv_z2,
fx * pc[1] * inv_z,
0,
-fy * inv_z,
fy * pc[1] * inv_z2,
fy + fy * pc[1] * pc[1] * inv_z2,
-fy * pc[0] * pc[1] * inv_z2,
-fy * pc[0] * inv_z;
H += J.transpose() * J;
b += -J.transpose() * e;
}
Vector6d dx;
dx = H.ldlt().solve(b);
if (isnan(dx[0])) {
cout << "result is nan!" << endl;
break;
}
if (iter > 0 && cost >= lastCost) {
// cost increase, update is not good
cout << "cost: " << cost << ", last cost: " << lastCost << endl;
break;
}
// update your estimation
pose = Sophus::SE3d::exp(dx) * pose;
lastCost = cost;
cout << "iteration " << iter << " cost=" << std::setprecision(12) << cost << endl;
if (dx.norm() < 1e-6) {
// converge
break;
}
}
cout << "pose by g-n: \n" << pose.matrix() << endl;
}
//
/ 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 { return true; }
//
// virtual bool write(ostream &out) const override { return true; }
//};
//
//class EdgeProjection : public g2o::BaseUnaryEdge<2, Eigen::Vector2d, VertexPose> {
//public:
// EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
//
// EdgeProjection(const Eigen::Vector3d &pos, const Eigen::Matrix3d &K) : _pos3d(pos), _K(K) {}
//
// virtual void computeError() override {
// const VertexPose *v = static_cast<VertexPose *> (_vertices[0]);
// Sophus::SE3d T = v->estimate();
// Eigen::Vector3d pos_pixel = _K * (T * _pos3d);
// pos_pixel /= pos_pixel[2];
// _error = _measurement - pos_pixel.head<2>();
// }
//
// virtual void linearizeOplus() override {
// const VertexPose *v = static_cast<VertexPose *> (_vertices[0]);
// Sophus::SE3d T = v->estimate();
// Eigen::Vector3d pos_cam = T * _pos3d;
// double fx = _K(0, 0);
// double fy = _K(1, 1);
// double cx = _K(0, 2);
// double cy = _K(1, 2);
// double X = pos_cam[0];
// double Y = pos_cam[1];
// double Z = pos_cam[2];
// double Z2 = Z * Z;
// _jacobianOplusXi
// << -fx / Z, 0, fx * X / Z2, fx * X * Y / Z2, -fx - fx * X * X / Z2, fx * Y / Z,
// 0, -fy / Z, fy * Y / (Z * Z), fy + fy * Y * Y / Z2, -fy * X * Y / Z2, -fy * X / Z;
// }
//
// virtual bool read(istream &in) override { return true; }
//
// virtual bool write(ostream& out) const override { return true; }
//
//private:
// Eigen::Vector3d _pos3d;
// Eigen::Matrix3d _K;
//};
void bundleAdjustmentG2O(
const VecVector3d &points_3d,
const VecVector2d &points_2d,
const Mat &K,
Sophus::SE3d &pose) {
//g2o::BlockSolver_6_3;
// 构建图优化，先设定g2o
typedef g2o::BlockSolver<g2o::BlockSolverTraits<6, 3>> BlockSolverType; // pose is 6, landmark is 3
typedef g2o::LinearSolverDense<BlockSolverType::PoseMatrixType> LinearSolverType; // 线性求解器类型
// 梯度下降方法，可以从GN, LM, DogLeg 中选
auto solver = new g2o::OptimizationAlgorithmLevenberg(
g2o::make_unique<BlockSolverType>(g2o::make_unique<LinearSolverType>()));
g2o::SparseOptimizer optimizer; // 图模型
optimizer.setAlgorithm(solver); // 设置求解器
optimizer.setVerbose(true); // 打开调试输出
//这里不用SLAM14讲里自定义的顶点和边了
//g2o::VertexSE3Expmap;
//g2o::EdgeSE3ProjectXYZOnlyPose;
// vertex
g2o::VertexSE3Expmap*vertex_pose = new g2o::VertexSE3Expmap(); // camera vertex_pose
vertex_pose->setId(0);
vertex_pose->setEstimate(g2o::SE3Quat());
vertex_pose->setFixed(false);
optimizer.addVertex(vertex_pose);
// K
Eigen::Matrix3d K_eigen;
K_eigen <<
K.at<double>(0, 0), K.at<double>(0, 1), K.at<double>(0, 2),
K.at<double>(1, 0), K.at<double>(1, 1), K.at<double>(1, 2),
K.at<double>(2, 0), K.at<double>(2, 1), K.at<double>(2, 2);
// 添加相机参数 parameter: camera intrinsics
g2o::CameraParameters* camera = new g2o::CameraParameters(
K.at<double>(0, 0), Eigen::Vector2d(K.at<double>(0, 2), K.at<double>(1, 2)), 0);
camera->setId(0);
optimizer.addParameter(camera);
// edges
int index = 1;
for (size_t i = 0; i < points_2d.size(); ++i) {
auto p2d = points_2d[i];
auto p3d = points_3d[i];
g2o::VertexSBAPointXYZ* mappoint = new g2o::VertexSBAPointXYZ();
//因为id为0的是相机顶点
mappoint->setId(i + 1);
mappoint->setEstimate(p3d);
mappoint->setMarginalized(true);
//一直不明白为什么要setfixed
mappoint->setFixed(true);
optimizer.addVertex(mappoint);
g2o::EdgeProjectXYZ2UV* edge = new g2o::EdgeProjectXYZ2UV();
edge->setVertex(0, mappoint);
edge->setVertex(1, vertex_pose);
edge->setMeasurement(p2d);
edge->setInformation(Eigen::Matrix2d::Identity());
edge->setParameterId(0, 0);
edge->setRobustKernel(new g2o::RobustKernelHuber());
optimizer.addEdge(edge);
//g2o::EdgeSE3ProjectXYZOnlyPose* edge = new g2o::EdgeSE3ProjectXYZOnlyPose();
//edge->setId(index);
//edge->setVertex(0, vertex_pose);
//edge->setMeasurement(p2d);
//edge->setInformation(Eigen::Matrix2d::Identity());
//edge->fx = K.at<double>(0, 0);
//edge->fy = K.at<double>(1, 1);
//edge->cx = K.at<double>(0, 2);
//edge->cy = K.at<double>(1, 2);
//edge->Xw = p3d;
//optimizer.addEdge(edge);
//index++;
//auto p2d = points_2d[i];
//auto p3d = points_3d[i];
//EdgeProjection *edge = new EdgeProjection(p3d, K_eigen);
//edge->setId(index);
//edge->setVertex(0, vertex_pose);
//edge->setMeasurement(p2d);
//edge->setInformation(Eigen::Matrix2d::Identity());
//optimizer.addEdge(edge);
//index++;
}
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
optimizer.setVerbose(true);
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 << "pose estimated by g2o =\n" << vertex_pose->estimate().to_homogeneous_matrix() << endl;
//pose = vertex_pose->estimate();
}
总结
以上三种方法都是同一个任务，只优化本帧相机位姿，不优化路标3D点，第一种方法是用一元边，把3D点和2D点都给这个一元边，然后它计算误差
第二种和第三种都一样，都是定义二元边，只是都必须要把路标3D点设置为定值，不优化它，否则程序出错，至今我还不知道为什么，目前看来EdgeProjectXYZ2UV和EdgeSE3ProjectXYZ在这个任务下是一样的
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