在这篇文章中,记录的是我对《视觉SLAM十四讲》第七讲——视觉里程计1实践部分中,三角测量代码的理解以及相关函数的解释。
理论方面的知识可以见我另一篇博客:
《视觉SLAM十四讲》ch7学习笔记(1)—— 视觉里程计1_sticker_阮的博客-CSDN博客_视觉slam十四讲ch7
关于2D-2D点求解相机位姿可参考我另一篇博客:
《视觉SLAM十四讲》ch7学习笔记(2)——实践部分对数约束求解相机运动的代码解析_sticker_阮的博客-CSDN博客
代码的主要架构如下:
代码源码以及解析如下:
#include
#include
#include
// #include "extra.h" // used in opencv2
using namespace std;
using namespace cv;
//自定义的函数声明
void find_feature_matches(
const Mat &img_1, const Mat &img_2,
std::vector &keypoints_1,
std::vector &keypoints_2,
std::vector &matches);
void pose_estimation_2d2d(
const std::vector &keypoints_1,
const std::vector &keypoints_2,
const std::vector &matches,
Mat &R, Mat &t);
void triangulation(
const vector &keypoint_1,
const vector &keypoint_2,
const std::vector &matches,
const Mat &R, const Mat &t,
vector &points
);
/// 作图用
inline cv::Scalar get_color(float depth) {
float up_th = 50, low_th = 10, th_range = up_th - low_th;
if (depth > up_th) depth = up_th;
if (depth < low_th) depth = low_th;
return cv::Scalar(255 * depth / th_range, 0, 255 * (1 - depth / th_range));
}
// 像素坐标转相机归一化坐标
Point2f pixel2cam(const Point2d &p, const Mat &K);
int main(int argc, char **argv) {
if (argc != 3) {
cout << "usage: triangulation img1 img2" << endl;
return 1;
}
chrono::steady_clock::time_point t1=chrono::steady_clock::now();
//-- 读取图像
Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR); //前面引号里的是图片的位置
Mat img_2 = imread(argv[2], CV_LOAD_IMAGE_COLOR);
vector keypoints_1, keypoints_2;
vector 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;
//-- 估计两张图像间运动
Mat R, t;
pose_estimation_2d2d(keypoints_1, keypoints_2, matches, R, t);
//-- 三角化
vector points;
triangulation(keypoints_1, keypoints_2, matches, R, t, points);
//-- 验证三角化点与特征点的重投影关系
Mat K = (Mat_(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);
Mat img1_plot = img_1.clone();
Mat img2_plot = img_2.clone();
for (int i = 0; i < matches.size(); i++) {
// 第一个图
float depth1 = points[i].z;
cout <<"第"<(3, 1) << points[i].x, points[i].y, points[i].z) + t;
float depth2 = pt2_trans.at(2, 0);
cv::circle(img2_plot, keypoints_2[matches[i].trainIdx].pt, 2, get_color(depth2), 2);
}
cv::imshow("img 1", img1_plot);
cv::imshow("img 2", img2_plot);
chrono::steady_clock::time_point t2=chrono::steady_clock::now();
chrono::durationtime_used=chrono::duration_cast>(t2-t1);
cout<<"time spent by project: "< &keypoints_1,
std::vector &keypoints_2,
std::vector &matches) {
//-- 初始化
Mat descriptors_1, descriptors_2;
// used in OpenCV3
Ptr detector = ORB::create();
Ptr descriptor = ORB::create();
// use this if you are in OpenCV2
// Ptr detector = FeatureDetector::create ( "ORB" );
// Ptr descriptor = DescriptorExtractor::create ( "ORB" );
Ptr 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 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]);
}
}
}
void pose_estimation_2d2d(
const std::vector &keypoints_1,
const std::vector &keypoints_2,
const std::vector &matches,
Mat &R, Mat &t) {
// 相机内参,TUM Freiburg2
Mat K = (Mat_(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);//内参矩阵
//-- 把匹配点转换为vector的形式
vector points1;
vector points2;
for (int i = 0; i < (int) matches.size(); i++) {
points1.push_back(keypoints_1[matches[i].queryIdx].pt);
points2.push_back(keypoints_2[matches[i].trainIdx].pt);
}
//-- 计算本质矩阵
Point2d principal_point(325.1, 249.7); //相机主点, TUM dataset标定值
int focal_length = 521; //相机焦距, TUM dataset标定值
Mat essential_matrix;
essential_matrix = findEssentialMat(points1, points2, focal_length, principal_point);
//-- 从本质矩阵中恢复旋转和平移信息.
recoverPose(essential_matrix, points1, points2, R, t, focal_length, principal_point);
cout<<"R 矩阵:"< &keypoint_1,
const vector &keypoint_2,
const std::vector &matches,
const Mat &R, const Mat &t,
vector &points) {
//T1和T2矩阵是相机位姿矩阵
Mat T1 = (Mat_(3, 4) <<
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0);
Mat T2 = (Mat_(3, 4) <<
R.at(0, 0), R.at(0, 1), R.at(0, 2), t.at(0, 0),
R.at(1, 0), R.at(1, 1), R.at(1, 2), t.at(1, 0),
R.at(2, 0), R.at(2, 1), R.at(2, 2), t.at(2, 0)
);
Mat K = (Mat_(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);
vector pts_1, pts_2;
for(int i=0;i(3, 0); // 归一化 同时除以最后一维数
Point3d p(
x.at(0, 0),
x.at(1, 0),
x.at(2, 0)
);
points.push_back(p);
}
}
Point2f pixel2cam(const Point2d &p, const Mat &K) {
return Point2f
(
(p.x - K.at(0, 2)) / K.at(0, 0),
(p.y - K.at(1, 2)) / K.at(1, 1)
);
}
(1)cv::triangulatePoints()函数
cv::triangulatePoints(T1, T2, pts_1, pts_2, pts_4d);
其中的四个参数分别表示:T1,T2是两个相机的位姿,pts_1,pts_2是特征点在两个相机坐标系下的坐标,pts_4d是输出三角化后的特征点的3D坐标,是四维的奇次坐标。因此需要将前三个维度除以第四个维度以得到非齐次坐标xyz。
(2)cv::circle()函数
函数原型
CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius,
const Scalar& color, int thickness = 1,
int lineType = LINE_8, int shift = 0);
最后一个参数shift指的是坐标和半径小数点的位数,如果设为1,就相当于对坐标和半径值右移了1位,画的圆的实际位置和半径都变为设定值的一半。
作用:在制定图像上,以某个像素点为圆心画圆。