1. 首页
  2. 文章中心
  3. slam

视觉SLAM十四讲 第七讲 视觉里程计1 3D-3D位姿求解 代码解析

63 阅读 0 评论
我是靠谱客的博主 威武往事，最近开发中收集的这篇文章主要介绍视觉SLAM十四讲 第七讲 视觉里程计1 3D-3D位姿求解 代码解析，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

总体思路

  1. 对两幅图像img_1，img_2提取特征点
  2. 特征点匹配
  3. 通过相机内参求取特征点对应的相机坐标X,Y，获取特征点的深度信息Z。
  4. 通过SVD分解，求除相机的位姿R，t。
    1. 计算质心p1 += pts1[i];p1 = Point3f(Vec3f(p1) / N);
    2. 去质心q1[i] = pts1[i] - p1;q2[i] = pts2[i] - p2;
    3. 构建W矩阵q1*q2^T
    4. 对W矩阵进行SVD分解，得到U，V矩阵
    5. 由UV矩阵，获得旋转矩阵 R = U V T R=UV^T R=UVT
    6. 由 t ∗ = p − R p ′ t^{*}=p-Rp' t∗=p−Rp′得到平移矩阵。
  5. 由R，t和两组匹配到的特征点的相机坐标，进行BA优化。
    1. 顶点（优化变量）
      1. 相机位姿_estimate = Sophus::SE3d();
      2. 相机位姿的增量_estimate = Sophus::SE3d::exp(update_eigen) * _estimate;
    2. 边（误差项）
      1. 由相机位姿，将第一个相机的特征点相机坐标，投影到第二个相机的相机坐标下，与提取特征点 经相机内参生成的位姿作差_error = _measurement - pose->estimate() * _point;
      2. 单个误差关于位姿的导数
        1. 3D坐标对相机位姿的误差
          ∂ P ′ ∂ δ ξ = ∂ ( T P ) ∂ δ ξ = ( T P ) ⊙ = [ I − P ′ ∧ 0 T 0 T ] frac{partial P'}{partial delta xi}=frac{partial(TP)}{partial delta xi}=(TP)^odot=left[ begin{matrix}I&-P'^{wedge}\0^T&0^Tend{matrix}right] ∂δξ∂P′​=∂δξ∂(TP)​=(TP)⊙=[I0T​−P′∧0T​]
        2. 因此，使用李代数扰动模型
          ∂ e ∂ δ ξ = − ( e x p ( ξ ∧ ) p i ′ ) ⊙ = [ − I P ′ ∧ 0 T 0 T ] frac{partial e}{partial delta xi}=-(exp(xi ^{wedge})p_i')^odot=left[ begin{matrix}-I&P'^{wedge}\0^T&0^Tend{matrix}right] ∂δξ∂e​=−(exp(ξ∧)pi′​)⊙=[−I0T​P′∧0T​]
    3. g2o库求解过程
      optimizer.addVertex(pose);
      对每个特征点增加optimizer.addEdge(edge);
      optimizer.initializeOptimization();
      optimizer.optimize(10);

代码参考


#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);
// 像素坐标转相机归一化坐标
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;
}
//-- 读取图像
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() << "组匹配点" << endl;
// 建立3D点
Mat depth1 = imread(argv[3], CV_LOAD_IMAGE_UNCHANGED);
// 深度图为16位无符号数，单通道图像
Mat depth2 = imread(argv[4], CV_LOAD_IMAGE_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> 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;
// 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 pose_estimation_3d3d(const vector<Point3f> &pts1,
const vector<Point3f> &pts2,
Mat &R, Mat &t) {
Point3f p1, p2;
// center of mass
int N = pts1.size();
for (int i = 0; i < N; i++) {
p1 += pts1[i];
p2 += pts2[i];
}
p1 = Point3f(Vec3f(p1) / N);
p2 = Point3f(Vec3f(p2) / N);
vector<Point3f> q1(N), q2(N); // remove the center
for (int i = 0; i < N; i++) {
q1[i] = pts1[i] - p1;
q2[i] = pts2[i] - p2;
}
// compute q1*q2^T
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;
Eigen::Matrix3d R_ = U * (V.transpose());
if (R_.determinant() < 0) {
R_ = -R_;
}
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) {
// 构建图优化，先设定g2o
typedef g2o::BlockSolverX BlockSolverType;
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
// 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));
}

参考资料

高翔《视觉SLAM十四讲：从理论到实践》及代码
2. 笔记，视觉SLAM十四讲 第七讲 视觉里程计1 Bundle Adjustment公式推导：

最后

以上就是威武往事为你收集整理的视觉SLAM十四讲 第七讲 视觉里程计1 3D-3D位姿求解 代码解析的全部内容，希望文章能够帮你解决视觉SLAM十四讲 第七讲 视觉里程计1 3D-3D位姿求解 代码解析所遇到的程序开发问题。

如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。

本图文内容来源于网友提供，作为学习参考使用，或来自网络收集整理，版权属于原作者所有。
点赞(42)

相关文章

视觉里程计02 基于特征匹配的位姿估计概述测试代码
视觉里程计02 基于特征匹配的位姿估计概述测试代码
ORB-SLAM2中双目情况下的处理
ORB-SLAM2中双目情况下的处理
视觉里程计- 位姿
视觉里程计- 位姿
vslam从入门到入土：使用D435的orb-slam2在ubuntu18中的安装0.更新所有库1.ROS2.Pangolin3. Realsense安装4. orb-slam2 安装5. 摄像头在ros中启动测试orb-slam2运行：
vslam从入门到入土：使用D435的orb-slam2在ubuntu18中的安装0.更新所有库1.ROS2.Pangolin3. Realsense安装4. orb-slam2 安装5. 摄像头在ros中启动测试orb-slam2运行：
视觉SLAM十四讲 第七讲 视觉里程计1 3D-3D位姿求解 代码解析
视觉SLAM十四讲 第七讲 视觉里程计1 3D-3D位姿求解 代码解析
开源视觉SLAM方案对比
开源视觉SLAM方案对比
RGBD VIO－RGBD相机预积分紧耦合
RGBD VIO－RGBD相机预积分紧耦合
OpenCV入门（八）--形态学图像处理
OpenCV入门（八）--形态学图像处理

评论列表共有 0 条评论

立即
投稿
返回
顶部