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liuyancheng 2021-09-22 19:42:25 +08:00
parent a55d24a467
commit 6117fcf244
4 changed files with 1003 additions and 703 deletions
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.gitignore vendored
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@ -47,3 +47,4 @@ cmake-build-debug/
*.pyc
*.osa
.vscode/settings.json

311
src/CameraModels/KannalaBrandt8.cpp
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@ -20,9 +20,22 @@
#include <boost/serialization/export.hpp>
namespace ORB_SLAM3 {
namespace ORB_SLAM3
{
cv::Point2f KannalaBrandt8::project(const cv::Point3f &p3D) {
/**
* @brief 投影
* xc​ = Xc/Zc, yc = Yc/Zc
* r^2 = xc^2 + yc^2
* θ = arctan(r)
* θd = k0*θ + k1*θ^3 + k2*θ^5 + k3*θ^7 + k4*θ^9
* xd = θd/r * xc yd = θd/r * yc
* u = fx*xd + cx v = fy*yd + cy
* @param p3D 三维点
* @return 像素坐标
*/
cv::Point2f KannalaBrandt8::project(const cv::Point3f &p3D)
{
const float x2_plus_y2 = p3D.x * p3D.x + p3D.y * p3D.y;
const float theta = atan2f(sqrtf(x2_plus_y2), p3D.z);
const float psi = atan2f(p3D.y, p3D.x);
@ -32,25 +45,41 @@ namespace ORB_SLAM3 {
const float theta5 = theta3 * theta2;
const float theta7 = theta5 * theta2;
const float theta9 = theta7 * theta2;
const float r = theta + mvParameters[4] * theta3 + mvParameters[5] * theta5
+ mvParameters[6] * theta7 + mvParameters[7] * theta9;
const float r = theta + mvParameters[4] * theta3 + mvParameters[5] * theta5 + mvParameters[6] * theta7 + mvParameters[7] * theta9;
return cv::Point2f(mvParameters[0] * r * cos(psi) + mvParameters[2],
mvParameters[1] * r * sin(psi) + mvParameters[3]);
}
cv::Point2f KannalaBrandt8::project(const cv::Matx31f &m3D) {
/**
* @brief 投影
* @param m3D 三维点
* @return 像素坐标
*/
cv::Point2f KannalaBrandt8::project(const cv::Matx31f &m3D)
{
return this->project(cv::Point3f(m3D(0), m3D(1), m3D(2)));
}
cv::Point2f KannalaBrandt8::project(const cv::Mat &m3D) {
/**
* @brief 投影
* @param m3D 三维点
* @return 像素坐标
*/
cv::Point2f KannalaBrandt8::project(const cv::Mat &m3D)
{
const float *p3D = m3D.ptr<float>();
return this->project(cv::Point3f(p3D[0], p3D[1], p3D[2]));
}
Eigen::Vector2d KannalaBrandt8::project(const Eigen::Vector3d &v3D) {
/**
* @brief 投影
* @param m3D 三维点
* @return 像素坐标
*/
Eigen::Vector2d KannalaBrandt8::project(const Eigen::Vector3d &v3D)
{
const double x2_plus_y2 = v3D[0] * v3D[0] + v3D[1] * v3D[1];
const double theta = atan2f(sqrtf(x2_plus_y2), v3D[2]);
const double psi = atan2f(v3D[1], v3D[0]);
@ -60,17 +89,22 @@ namespace ORB_SLAM3 {
const double theta5 = theta3 * theta2;
const double theta7 = theta5 * theta2;
const double theta9 = theta7 * theta2;
const double r = theta + mvParameters[4] * theta3 + mvParameters[5] * theta5
+ mvParameters[6] * theta7 + mvParameters[7] * theta9;
const double r = theta + mvParameters[4] * theta3 + mvParameters[5] * theta5 + mvParameters[6] * theta7 + mvParameters[7] * theta9;
Eigen::Vector2d res;
res[0] = mvParameters[0] * r * cos(psi) + mvParameters[2];
res[1] = mvParameters[1] * r * sin(psi) + mvParameters[3];
res[0] = mvParameters[0] * r * cos(psi) + mvParameters[2]; // cos(psi) = xc / r
res[1] = mvParameters[1] * r * sin(psi) + mvParameters[3]; // sin(psi) = yc / r
return res;
}
cv::Mat KannalaBrandt8::projectMat(const cv::Point3f &p3D) {
/**
* @brief 投影
* @param m3D 三维点
* @return 像素坐标
*/
cv::Mat KannalaBrandt8::projectMat(const cv::Point3f &p3D)
{
cv::Point2f point = this->project(p3D);
cv::Mat ret = (cv::Mat_<float>(2, 1) << point.x, point.y);
return ret.clone();
@ -81,32 +115,68 @@ namespace ORB_SLAM3 {
return 1.f;
}
cv::Mat KannalaBrandt8::unprojectMat(const cv::Point2f &p2D){
/**
* @brief 反投影,以mat形式返回，KannalaBrandt8::TriangulateMatches中调用
* @param p2D 特征点
* @return
*/
cv::Mat KannalaBrandt8::unprojectMat(const cv::Point2f &p2D)
{
cv::Point3f ray = this->unproject(p2D);
cv::Mat ret = (cv::Mat_<float>(3, 1) << ray.x, ray.y, ray.z);
return ret.clone();
}
cv::Matx31f KannalaBrandt8::unprojectMat_(const cv::Point2f &p2D) {
/**
* @brief 反投影
* @param p2D 特征点
* @return
*/
cv::Matx31f KannalaBrandt8::unprojectMat_(const cv::Point2f &p2D)
{
cv::Point3f ray = this->unproject(p2D);
cv::Matx31f r{ray.x, ray.y, ray.z};
return r;
}
cv::Point3f KannalaBrandt8::unproject(const cv::Point2f &p2D) {
/**
* @brief 反投影
* 已知u与v 未矫正的特征点像素坐标
* xd = (u - cx) / fx yd = (v - cy) / fy
* 待求的 xc = xd * tan(θ) / θd yc = yd * tan(θ) / θd
* 其中 θd的算法如下：
* xd^2 + yd^2 = θd^2/r^2 * (xc^2 + yc^2)
* θd = sqrt(xd^2 + yd^2) / sqrt(xc^2 + yc^2) * r
* 其中r = sqrt(xc^2 + yc^2)
* 所以 θd = sqrt(xd^2 + yd^2) 已知
* 所以待求的只有tan(θ),也就是θ
* 这里θd = θ + k1*θ^3 + k2*θ^5 + k3*θ^7 + k4*θ^9
* 直接求解θ比较麻烦，这里用迭代的方式通过误差的一阶导数求θ
* θ的初始值定为了θd，设θ + k1*θ^3 + k2*θ^5 + k3*θ^7 + k4*θ^9 = f(θ)
* e(θ) = f(θ) - θd 目标是求解一个θ值另e(θ) = 0
* 泰勒展开e(θ+δθ) = e(θ) + e`(θ) * δθ = 0
* e`(θ) = 1 + 3*k1*θ^*2 + 5*k2*θ^4 + 7*k3*θ^6 + 8*k4*θ^8
* δθ = -e(θ)/e`(θ) 经过多次迭代可求得θ
* 最后xc = xd * tan(θ) / θd yc = yd * tan(θ) / θd
* @param p2D 特征点
* @return
*/
cv::Point3f KannalaBrandt8::unproject(const cv::Point2f &p2D)
{
//Use Newthon method to solve for theta with good precision (err ~ e-6)
cv::Point2f pw((p2D.x - mvParameters[2]) / mvParameters[0], (p2D.y - mvParameters[3]) / mvParameters[1]);
cv::Point2f pw((p2D.x - mvParameters[2]) / mvParameters[0], (p2D.y - mvParameters[3]) / mvParameters[1]); // xd yd
float scale = 1.f;
float theta_d = sqrtf(pw.x * pw.x + pw.y * pw.y);
theta_d = fminf(fmaxf(-CV_PI / 2.f, theta_d), CV_PI / 2.f);
float theta_d = sqrtf(pw.x * pw.x + pw.y * pw.y); // sin(psi) = yc / r
theta_d = fminf(fmaxf(-CV_PI / 2.f, theta_d), CV_PI / 2.f); // 不能超过180度
if (theta_d > 1e-8) {
if (theta_d > 1e-8)
{
//Compensate distortion iteratively
float theta = theta_d;
for (int j = 0; j < 10; j++) {
float theta2 = theta * theta, theta4 = theta2 * theta2, theta6 = theta4 * theta2, theta8 =
theta4 * theta4;
// 开始迭代
for (int j = 0; j < 10; j++)
{
float theta2 = theta * theta, theta4 = theta2 * theta2, theta6 = theta4 * theta2, theta8 = theta4 * theta4;
float k0_theta2 = mvParameters[4] * theta2, k1_theta4 = mvParameters[5] * theta4;
float k2_theta6 = mvParameters[6] * theta6, k3_theta8 = mvParameters[7] * theta8;
float theta_fix = (theta * (1 + k0_theta2 + k1_theta4 + k2_theta6 + k3_theta8) - theta_d) /
@ -116,13 +186,25 @@ namespace ORB_SLAM3 {
break;
}
//scale = theta - theta_d;
// 求得tan(θ) / θd
scale = std::tan(theta) / theta_d;
}
return cv::Point3f(pw.x * scale, pw.y * scale, 1.f);
}
cv::Mat KannalaBrandt8::projectJac(const cv::Point3f &p3D) {
/**
* @brief 求解三维点关于二维像素坐标的雅克比矩阵
* u = fx * θd * x / sqrt(x^2 + y^2) + cx
* v = fy * θd * y / sqrt(x^2 + y^2) + cy
* 这两个式子分别对 xyz 求导
* θd = θ + k1*θ^3 + k2*θ^5 + k3*θ^7 + k4*θ^9
* θ = arctan(sqrt(x^2 + y^2), z)
* @param p3D 三维点
* @return
*/
cv::Mat KannalaBrandt8::projectJac(const cv::Point3f &p3D)
{
float x2 = p3D.x * p3D.x, y2 = p3D.y * p3D.y, z2 = p3D.z * p3D.z;
float r2 = x2 + y2;
float r = sqrt(r2);
@ -140,23 +222,34 @@ namespace ORB_SLAM3 {
9 * mvParameters[7] * theta8;
cv::Mat Jac(2, 3, CV_32F);
Jac.at<float>(0, 0) = mvParameters[0] * (fd * p3D.z * x2 / (r2 * (r2 + z2)) + f * y2 / r3);
Jac.at<float>(0, 0) = mvParameters[0] * (fd * p3D.z * x2 / (r2 * (r2 + z2)) + f * y2 / r3); // ∂u/∂x
Jac.at<float>(1, 0) =
mvParameters[1] * (fd * p3D.z * p3D.y * p3D.x / (r2 * (r2 + z2)) - f * p3D.y * p3D.x / r3);
mvParameters[1] * (fd * p3D.z * p3D.y * p3D.x / (r2 * (r2 + z2)) - f * p3D.y * p3D.x / r3); // ∂v/∂x
Jac.at<float>(0, 1) =
mvParameters[0] * (fd * p3D.z * p3D.y * p3D.x / (r2 * (r2 + z2)) - f * p3D.y * p3D.x / r3);
Jac.at<float>(1, 1) = mvParameters[1] * (fd * p3D.z * y2 / (r2 * (r2 + z2)) + f * x2 / r3);
mvParameters[0] * (fd * p3D.z * p3D.y * p3D.x / (r2 * (r2 + z2)) - f * p3D.y * p3D.x / r3); // ∂u/∂y
Jac.at<float>(1, 1) = mvParameters[1] * (fd * p3D.z * y2 / (r2 * (r2 + z2)) + f * x2 / r3); // ∂v/∂y
Jac.at<float>(0, 2) = -mvParameters[0] * fd * p3D.x / (r2 + z2);
Jac.at<float>(1, 2) = -mvParameters[1] * fd * p3D.y / (r2 + z2);
Jac.at<float>(0, 2) = -mvParameters[0] * fd * p3D.x / (r2 + z2); // ∂u/∂z
Jac.at<float>(1, 2) = -mvParameters[1] * fd * p3D.y / (r2 + z2); // ∂v/∂z
std::cout << "CV JAC: " << Jac << std::endl;
return Jac.clone();
}
Eigen::Matrix<double, 2, 3> KannalaBrandt8::projectJac(const Eigen::Vector3d &v3D) {
/**
* @brief 求解三维点关于二维像素坐标的雅克比矩阵,与上面相同，只不过输出类型不一样
* u = fx * θd * x / sqrt(x^2 + y^2)
* v = fy * θd * y / sqrt(x^2 + y^2)
* 这两个式子分别对 xyz 求导
* θd = θ + k1*θ^3 + k2*θ^5 + k3*θ^7 + k4*θ^9
* θ = arctan(sqrt(x^2 + y^2), z)
* @param p3D 三维点
* @return
*/
Eigen::Matrix<double, 2, 3> KannalaBrandt8::projectJac(const Eigen::Vector3d &v3D)
{
double x2 = v3D[0] * v3D[0], y2 = v3D[1] * v3D[1], z2 = v3D[2] * v3D[2];
double r2 = x2 + y2;
double r = sqrt(r2);
@ -188,13 +281,25 @@ namespace ORB_SLAM3 {
return JacGood;
}
cv::Mat KannalaBrandt8::unprojectJac(const cv::Point2f &p2D) {
cv::Mat KannalaBrandt8::unprojectJac(const cv::Point2f &p2D)
{
return cv::Mat();
}
/** 三角化恢复三维点，但要提前做去畸变 单目初始化中使用
* @param vKeys1 第一帧的关键点
* @param vKeys2 第二帧的关键点
* @param vMatches12 匹配关系，长度与vKeys1一样，对应位置存放vKeys2中关键点的下标
* @param R21 顾名思义
* @param t21 顾名思义
* @param vP3D 恢复出的三维点
* @param vbTriangulated 是否三角化成功，用于统计匹配点数量
*/
bool KannalaBrandt8::ReconstructWithTwoViews(const std::vector<cv::KeyPoint> &vKeys1, const std::vector<cv::KeyPoint> &vKeys2, const std::vector<int> &vMatches12,
cv::Mat &R21, cv::Mat &t21, std::vector<cv::Point3f> &vP3D, std::vector<bool> &vbTriangulated){
if(!tvr){
cv::Mat &R21, cv::Mat &t21, std::vector<cv::Point3f> &vP3D, std::vector<bool> &vbTriangulated)
{
if (!tvr)
{
cv::Mat K = this->toK();
tvr = new TwoViewReconstruction(K);
}
@ -203,8 +308,10 @@ namespace ORB_SLAM3 {
std::vector<cv::KeyPoint> vKeysUn1 = vKeys1, vKeysUn2 = vKeys2;
std::vector<cv::Point2f> vPts1(vKeys1.size()), vPts2(vKeys2.size());
for(size_t i = 0; i < vKeys1.size(); i++) vPts1[i] = vKeys1[i].pt;
for(size_t i = 0; i < vKeys2.size(); i++) vPts2[i] = vKeys2[i].pt;
for (size_t i = 0; i < vKeys1.size(); i++)
vPts1[i] = vKeys1[i].pt;
for (size_t i = 0; i < vKeys2.size(); i++)
vPts2[i] = vKeys2[i].pt;
cv::Mat D = (cv::Mat_<float>(4, 1) << mvParameters[4], mvParameters[5], mvParameters[6], mvParameters[7]);
cv::Mat R = cv::Mat::eye(3, 3, CV_32F);
@ -212,39 +319,52 @@ namespace ORB_SLAM3 {
cv::fisheye::undistortPoints(vPts1, vPts1, K, D, R, K);
cv::fisheye::undistortPoints(vPts2, vPts2, K, D, R, K);
for(size_t i = 0; i < vKeys1.size(); i++) vKeysUn1[i].pt = vPts1[i];
for(size_t i = 0; i < vKeys2.size(); i++) vKeysUn2[i].pt = vPts2[i];
for (size_t i = 0; i < vKeys1.size(); i++)
vKeysUn1[i].pt = vPts1[i];
for (size_t i = 0; i < vKeys2.size(); i++)
vKeysUn2[i].pt = vPts2[i];
return tvr->Reconstruct(vKeysUn1, vKeysUn2, vMatches12, R21, t21, vP3D, vbTriangulated);
}
cv::Mat KannalaBrandt8::toK() {
/**
* @brief 返回内参矩阵
* @return K
*/
cv::Mat KannalaBrandt8::toK()
{
cv::Mat K = (cv::Mat_<float>(3, 3)
<< mvParameters[0], 0.f, mvParameters[2], 0.f, mvParameters[1], mvParameters[3], 0.f, 0.f, 1.f);
<< mvParameters[0],
0.f, mvParameters[2], 0.f, mvParameters[1], mvParameters[3], 0.f, 0.f, 1.f);
return K;
}
cv::Matx33f KannalaBrandt8::toK_() {
cv::Matx33f KannalaBrandt8::toK_()
{
cv::Matx33f K{mvParameters[0], 0.f, mvParameters[2], 0.f, mvParameters[1], mvParameters[3], 0.f, 0.f, 1.f};
return K;
}
bool KannalaBrandt8::epipolarConstrain(GeometricCamera* pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Mat &R12, const cv::Mat &t12, const float sigmaLevel, const float unc) {
bool KannalaBrandt8::epipolarConstrain(GeometricCamera *pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Mat &R12, const cv::Mat &t12, const float sigmaLevel, const float unc)
{
cv::Mat p3D;
return this->TriangulateMatches(pCamera2, kp1, kp2, R12, t12, sigmaLevel, unc, p3D) > 0.0001f;
}
bool KannalaBrandt8::epipolarConstrain_(GeometricCamera* pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Matx33f &R12, const cv::Matx31f &t12, const float sigmaLevel, const float unc) {
bool KannalaBrandt8::epipolarConstrain_(GeometricCamera *pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Matx33f &R12, const cv::Matx31f &t12, const float sigmaLevel, const float unc)
{
cv::Matx31f p3D;
return this->TriangulateMatches_(pCamera2, kp1, kp2, R12, t12, sigmaLevel, unc, p3D) > 0.0001f;
}
bool KannalaBrandt8::matchAndtriangulate(const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, GeometricCamera *pOther,
cv::Mat &Tcw1, cv::Mat &Tcw2,
const float sigmaLevel1, const float sigmaLevel2,
cv::Mat& x3Dtriangulated){
cv::Mat &x3Dtriangulated)
{
cv::Mat Rcw1 = Tcw1.colRange(0, 3).rowRange(0, 3);
cv::Mat Rwc1 = Rcw1.t();
cv::Mat tcw1 = Tcw1.rowRange(0, 3).col(3);
@ -273,7 +393,8 @@ namespace ORB_SLAM3 {
const float cosParallaxRays = ray1.dot(ray2) / (cv::norm(ray1) * cv::norm(ray2));
//If parallax is lower than 0.9998, reject this match
if(cosParallaxRays > 0.9998){
if (cosParallaxRays > 0.9998)
{
return false;
}
@ -294,13 +415,14 @@ namespace ORB_SLAM3 {
//Check triangulation in front of cameras
float z1 = Rcw1.row(2).dot(x3Dt) + tcw1.at<float>(2);
if(z1<=0){ //Point is not in front of the first camera
if (z1 <= 0)
{ //Point is not in front of the first camera
return false;
}
float z2 = Rcw2.row(2).dot(x3Dt) + tcw2.at<float>(2);
if(z2<=0){ //Point is not in front of the first camera
if (z2 <= 0)
{ //Point is not in front of the first camera
return false;
}
@ -312,7 +434,8 @@ namespace ORB_SLAM3 {
float errX1 = uv1.x - kp1.pt.x;
float errY1 = uv1.y - kp1.pt.y;
if((errX1*errX1+errY1*errY1)>5.991*sigmaLevel1){ //Reprojection error is high
if ((errX1 * errX1 + errY1 * errY1) > 5.991 * sigmaLevel1)
{ //Reprojection error is high
return false;
}
@ -324,7 +447,8 @@ namespace ORB_SLAM3 {
float errX2 = uv2.x - kp2.pt.x;
float errY2 = uv2.y - kp2.pt.y;
if((errX2*errX2+errY2*errY2)>5.991*sigmaLevel2){ //Reprojection error is high
if ((errX2 * errX2 + errY2 * errY2) > 5.991 * sigmaLevel2)
{ //Reprojection error is high
return false;
}
@ -335,16 +459,34 @@ namespace ORB_SLAM3 {
return true;
}
float KannalaBrandt8::TriangulateMatches(GeometricCamera *pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Mat &R12, const cv::Mat &t12, const float sigmaLevel, const float unc, cv::Mat& p3D) {
/**
* @brief 通过三角化后的重投影误差判断点匹配的情况，如果比较好则返回深度值
* @param pCamera2 右相机
* @param kp1 左相机特征点
* @param kp2 右相机特征点
* @param R12 2->1的旋转
* @param t12 2->1的平移
* @param sigmaLevel 特征点1的尺度的平方
* @param unc 特征点2的尺度的平方
* @param p3D 恢复的三维点
* @return 点的深度值
*/
float KannalaBrandt8::TriangulateMatches(GeometricCamera *pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Mat &R12, const cv::Mat &t12, const float sigmaLevel, const float unc, cv::Mat &p3D)
{
// 1. 得到对应特征点的相平面坐标
cv::Mat r1 = this->unprojectMat(kp1.pt);
cv::Mat r2 = pCamera2->unprojectMat(kp2.pt);
//Check parallax
// 2. 查看射线夹角
// 这里有点像极线约束，但并不是，将r2通过R12旋转到与r1同方向的坐标系
// 然后计算他们的夹角，看其是否超过1.14° 讲真，这个条件感觉有些苛刻
cv::Mat r21 = R12 * r2;
const float cosParallaxRays = r1.dot(r21) / (cv::norm(r1) * cv::norm(r21));
if(cosParallaxRays > 0.9998){
if (cosParallaxRays > 0.9998)
{
return -1;
}
@ -360,6 +502,7 @@ namespace ORB_SLAM3 {
p22.y = pr2[1];
cv::Mat x3D;
// 3. 设定变换矩阵用于三角化
cv::Mat Tcw1 = (cv::Mat_<float>(3, 4) << 1.f, 0.f, 0.f, 0.f,
0.f, 1.f, 0.f, 0.f,
0.f, 0.f, 1.f, 0.f);
@ -368,26 +511,32 @@ namespace ORB_SLAM3 {
cv::Mat t21 = -R21 * t12;
cv::hconcat(R21, t21, Tcw2);
// 4. 三角化
Triangulate(p11, p22, Tcw1, Tcw2, x3D);
cv::Mat x3Dt = x3D.t();
// 深度值是否正常
float z1 = x3D.at<float>(2);
if(z1 <= 0){
if (z1 <= 0)
{
return -1;
}
float z2 = R21.row(2).dot(x3Dt) + t21.at<float>(2);
if(z2<=0){
if (z2 <= 0)
{
return -1;
}
//Check reprojection error
// 5. 做下投影计算重投影误差
cv::Point2f uv1 = this->project(x3D);
float errX1 = uv1.x - kp1.pt.x;
float errY1 = uv1.y - kp1.pt.y;
if((errX1*errX1+errY1*errY1)>5.991 * sigmaLevel){ //Reprojection error is high
if ((errX1 * errX1 + errY1 * errY1) > 5.991 * sigmaLevel)
{ //Reprojection error is high
return -1;
}
@ -397,7 +546,8 @@ namespace ORB_SLAM3 {
float errX2 = uv2.x - kp2.pt.x;
float errY2 = uv2.y - kp2.pt.y;
if((errX2*errX2+errY2*errY2)>5.991 * unc){ //Reprojection error is high
if ((errX2 * errX2 + errY2 * errY2) > 5.991 * unc)
{ //Reprojection error is high
return -1;
}
@ -406,7 +556,8 @@ namespace ORB_SLAM3 {
return z1;
}
float KannalaBrandt8::TriangulateMatches_(GeometricCamera *pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Matx33f &R12, const cv::Matx31f &t12, const float sigmaLevel, const float unc, cv::Matx31f& p3D) {
float KannalaBrandt8::TriangulateMatches_(GeometricCamera *pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Matx33f &R12, const cv::Matx31f &t12, const float sigmaLevel, const float unc, cv::Matx31f &p3D)
{
cv::Matx31f r1 = this->unprojectMat_(kp1.pt);
cv::Matx31f r2 = pCamera2->unprojectMat_(kp2.pt);
@ -415,7 +566,8 @@ namespace ORB_SLAM3 {
const float cosParallaxRays = r1.dot(r21) / (cv::norm(r1) * cv::norm(r21));
if(cosParallaxRays > 0.9998){
if (cosParallaxRays > 0.9998)
{
return -1;
}
@ -445,12 +597,14 @@ namespace ORB_SLAM3 {
cv::Matx13f x3Dt = x3D.t();
float z1 = x3D(2);
if(z1 <= 0){
if (z1 <= 0)
{
return -1;
}
float z2 = R21.row(2).dot(x3Dt) + t21(2);
if(z2<=0){
if (z2 <= 0)
{
return -1;
}
@ -460,7 +614,8 @@ namespace ORB_SLAM3 {
float errX1 = uv1.x - kp1.pt.x;
float errY1 = uv1.y - kp1.pt.y;
if((errX1*errX1+errY1*errY1)>5.991 * sigmaLevel){ //Reprojection error is high
if ((errX1 * errX1 + errY1 * errY1) > 5.991 * sigmaLevel)
{ //Reprojection error is high
return -1;
}
@ -470,7 +625,8 @@ namespace ORB_SLAM3 {
float errX2 = uv2.x - kp2.pt.x;
float errY2 = uv2.y - kp2.pt.y;
if((errX2*errX2+errY2*errY2)>5.991 * unc){ //Reprojection error is high
if ((errX2 * errX2 + errY2 * errY2) > 5.991 * unc)
{ //Reprojection error is high
return -1;
}
@ -479,27 +635,43 @@ namespace ORB_SLAM3 {
return z1;
}
std::ostream & operator<<(std::ostream &os, const KannalaBrandt8 &kb) {
std::ostream &operator<<(std::ostream &os, const KannalaBrandt8 &kb)
{
os << kb.mvParameters[0] << " " << kb.mvParameters[1] << " " << kb.mvParameters[2] << " " << kb.mvParameters[3] << " "
<< kb.mvParameters[4] << " " << kb.mvParameters[5] << " " << kb.mvParameters[6] << " " << kb.mvParameters[7];
return os;
}
std::istream & operator>>(std::istream &is, KannalaBrandt8 &kb) {
std::istream &operator>>(std::istream &is, KannalaBrandt8 &kb)
{
float nextParam;
for(size_t i = 0; i < 8; i++){
for (size_t i = 0; i < 8; i++)
{
assert(is.good()); //Make sure the input stream is good
is >> nextParam;
kb.mvParameters[i] = nextParam;
}
return is;
}
/**
* @brief 通过三角化恢复归一化坐标的三维点坐标，该三维点为在左相机坐标系下的点
* @param p1 右相机
* @param p2 左相机特征点
* @param Tcw1 3×4 单位矩阵
* @param Tcw2 3×4 T21
* @param x3D 恢复的三维点
*/
void KannalaBrandt8::Triangulate(const cv::Point2f &p1, const cv::Point2f &p2, const cv::Mat &Tcw1, const cv::Mat &Tcw2, cv::Mat &x3D)
{
cv::Mat A(4, 4, CV_32F);
// 代码中有用到三角化的地方有：TwoViewReconstruction::Triangulate LocalMapping::CreateNewMapPoints KannalaBrandt8::Triangulate Initializer::Triangulate
// 见Initializer.cpp的Triangulate函数,A矩阵构建的方式类似，不同的是乘的反对称矩阵那个是像素坐标构成的，而这个是归一化坐标构成的
// Pc = Tcw*Pw， 左右两面乘Pc的反对称矩阵 [Pc]x * Tcw *Pw = 0 构成了A矩阵，中间涉及一个尺度a，因为都是归一化平面，但右面是0所以直接可以约掉不影响最后的尺度
// 0 -1 y Tcw.row(0) -Tcw.row(1) + y*Tcw.row(2)
// 1 0 -x * Tcw.row(1) = Tcw.row(0) - x*Tcw.row(2)
// -y x 0 Tcw.row(2) x*Tcw.row(1) - y*Tcw.row(0)
// 发现上述矩阵线性相关，所以取前两维，两个点构成了4行的矩阵，就是如下的操作，求出的是4维的结果[X,Y,Z,A]，所以需要除以最后一维使之为1，就成了[X,Y,Z,1]这种齐次形式
A.row(0) = p1.x * Tcw1.row(2) - Tcw1.row(0);
A.row(1) = p1.y * Tcw1.row(2) - Tcw1.row(1);
A.row(2) = p2.x * Tcw2.row(2) - Tcw2.row(0);
@ -515,7 +687,6 @@ namespace ORB_SLAM3 {
{
cv::Matx14f A0, A1, A2, A3;
A0 = p1.x * Tcw1.row(2) - Tcw1.row(0);
A1 = p1.y * Tcw1.row(2) - Tcw1.row(1);
A2 = p2.x * Tcw2.row(2) - Tcw2.row(0);

171
src/CameraModels/Pinhole.cpp
View File

@ -20,26 +20,51 @@
#include <boost/serialization/export.hpp>
namespace ORB_SLAM3 {
namespace ORB_SLAM3
{
long unsigned int GeometricCamera::nNextId = 0;
cv::Point2f Pinhole::project(const cv::Point3f &p3D) {
/**
* @brief 投影
* @param p3D 三维点
* @return 像素坐标
*/
cv::Point2f Pinhole::project(const cv::Point3f &p3D)
{
return cv::Point2f(mvParameters[0] * p3D.x / p3D.z + mvParameters[2],
mvParameters[1] * p3D.y / p3D.z + mvParameters[3]);
}
cv::Point2f Pinhole::project(const cv::Matx31f &m3D) {
/**
* @brief 投影
* @param m3D 三维点
* @return 像素坐标
*/
cv::Point2f Pinhole::project(const cv::Matx31f &m3D)
{
return this->project(cv::Point3f(m3D(0), m3D(1), m3D(2)));
}
cv::Point2f Pinhole::project(const cv::Mat &m3D) {
/**
* @brief 投影
* @param v3D 三维点
* @return 像素坐标
*/
cv::Point2f Pinhole::project(const cv::Mat &m3D)
{
const float *p3D = m3D.ptr<float>();
return this->project(cv::Point3f(p3D[0], p3D[1], p3D[2]));
}
Eigen::Vector2d Pinhole::project(const Eigen::Vector3d &v3D) {
/**
* @brief 投影
* @param p3D 三维点
* @return 像素坐标
*/
Eigen::Vector2d Pinhole::project(const Eigen::Vector3d &v3D)
{
Eigen::Vector2d res;
res[0] = mvParameters[0] * v3D[0] / v3D[2] + mvParameters[2];
res[1] = mvParameters[1] * v3D[1] / v3D[2] + mvParameters[3];
@ -47,33 +72,66 @@ namespace ORB_SLAM3 {
return res;
}
cv::Mat Pinhole::projectMat(const cv::Point3f &p3D) {
/**
* @brief 投影
* @param p3D 三维点
* @return 像素坐标
*/
cv::Mat Pinhole::projectMat(const cv::Point3f &p3D)
{
cv::Point2f point = this->project(p3D);
return (cv::Mat_<float>(2, 1) << point.x, point.y);
}
/**
* @brief 貌似是调试遗留的产物
*/
float Pinhole::uncertainty2(const Eigen::Matrix<double, 2, 1> &p2D)
{
return 1.0;
}
cv::Point3f Pinhole::unproject(const cv::Point2f &p2D) {
/**
* @brief 反投影
* @param p2D 特征点
* @return
*/
cv::Point3f Pinhole::unproject(const cv::Point2f &p2D)
{
return cv::Point3f((p2D.x - mvParameters[2]) / mvParameters[0], (p2D.y - mvParameters[3]) / mvParameters[1],
1.f);
}
cv::Mat Pinhole::unprojectMat(const cv::Point2f &p2D){
/**
* @brief 反投影
* @param p2D 特征点
* @return
*/
cv::Mat Pinhole::unprojectMat(const cv::Point2f &p2D)
{
cv::Point3f ray = this->unproject(p2D);
return (cv::Mat_<float>(3, 1) << ray.x, ray.y, ray.z);
}
cv::Matx31f Pinhole::unprojectMat_(const cv::Point2f &p2D) {
/**
* @brief 反投影
* @param p2D 特征点
* @return
*/
cv::Matx31f Pinhole::unprojectMat_(const cv::Point2f &p2D)
{
cv::Point3f ray = this->unproject(p2D);
cv::Matx31f r{ray.x, ray.y, ray.z};
return r;
}
cv::Mat Pinhole::projectJac(const cv::Point3f &p3D) {
/**
* @brief 求解三维点关于二维像素坐标的雅克比矩阵
* @param p3D 三维点
* @return
*/
cv::Mat Pinhole::projectJac(const cv::Point3f &p3D)
{
cv::Mat Jac(2, 3, CV_32F);
Jac.at<float>(0, 0) = mvParameters[0] / p3D.z;
Jac.at<float>(0, 1) = 0.f;
@ -85,7 +143,13 @@ namespace ORB_SLAM3 {
return Jac;
}
Eigen::Matrix<double, 2, 3> Pinhole::projectJac(const Eigen::Vector3d &v3D) {
/**
* @brief 求解三维点关于二维像素坐标的雅克比矩阵
* @param v3D 三维点
* @return
*/
Eigen::Matrix<double, 2, 3> Pinhole::projectJac(const Eigen::Vector3d &v3D)
{
Eigen::Matrix<double, 2, 3> Jac;
Jac(0, 0) = mvParameters[0] / v3D[2];
Jac(0, 1) = 0.f;
@ -97,7 +161,13 @@ namespace ORB_SLAM3 {
return Jac;
}
cv::Mat Pinhole::unprojectJac(const cv::Point2f &p2D) {
/**
* @brief 求解二维像素坐标关于三维点的雅克比矩阵
* @param p2D 特征点
* @return
*/
cv::Mat Pinhole::unprojectJac(const cv::Point2f &p2D)
{
cv::Mat Jac(3, 2, CV_32F);
Jac.at<float>(0, 0) = 1 / mvParameters[0];
Jac.at<float>(0, 1) = 0.f;
@ -109,9 +179,20 @@ namespace ORB_SLAM3 {
return Jac;
}
/** 三角化恢复三维点
* @param vKeys1 第一帧的关键点
* @param vKeys2 第二帧的关键点
* @param vMatches12 匹配关系，长度与vKeys1一样，对应位置存放vKeys2中关键点的下标
* @param R21 顾名思义
* @param t21 顾名思义
* @param vP3D 恢复出的三维点
* @param vbTriangulated 是否三角化成功，用于统计匹配点数量
*/
bool Pinhole::ReconstructWithTwoViews(const std::vector<cv::KeyPoint> &vKeys1, const std::vector<cv::KeyPoint> &vKeys2, const std::vector<int> &vMatches12,
cv::Mat &R21, cv::Mat &t21, std::vector<cv::Point3f> &vP3D, std::vector<bool> &vbTriangulated){
if(!tvr){
cv::Mat &R21, cv::Mat &t21, std::vector<cv::Point3f> &vP3D, std::vector<bool> &vbTriangulated)
{
if (!tvr)
{
cv::Mat K = this->toK();
tvr = new TwoViewReconstruction(K);
}
@ -119,20 +200,42 @@ namespace ORB_SLAM3 {
return tvr->Reconstruct(vKeys1, vKeys2, vMatches12, R21, t21, vP3D, vbTriangulated);
}
cv::Mat Pinhole::toK() {
/**
* @brief 返回内参矩阵
* @return K
*/
cv::Mat Pinhole::toK()
{
cv::Mat K = (cv::Mat_<float>(3, 3)
<< mvParameters[0], 0.f, mvParameters[2], 0.f, mvParameters[1], mvParameters[3], 0.f, 0.f, 1.f);
<< mvParameters[0],
0.f, mvParameters[2], 0.f, mvParameters[1], mvParameters[3], 0.f, 0.f, 1.f);
return K;
}
cv::Matx33f Pinhole::toK_() {
/**
* @brief 返回内参矩阵
* @return K
*/
cv::Matx33f Pinhole::toK_()
{
cv::Matx33f K{mvParameters[0], 0.f, mvParameters[2], 0.f, mvParameters[1], mvParameters[3], 0.f, 0.f, 1.f};
return K;
}
bool Pinhole::epipolarConstrain(GeometricCamera* pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Mat &R12, const cv::Mat &t12, const float sigmaLevel, const float unc) {
/**
* @brief 极线约束
* @param pCamera2 右相机
* @param kp1 左相机特征点
* @param kp2 右相机特征点
* @param R12 2->1的旋转
* @param t12 2->1的平移
* @param sigmaLevel 特征点1的尺度的平方
* @param unc 特征点2的尺度的平方
* @return 三维点恢复的成功与否
*/
bool Pinhole::epipolarConstrain(GeometricCamera *pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Mat &R12, const cv::Mat &t12, const float sigmaLevel, const float unc)
{
//Compute Fundamental Matrix
cv::Mat t12x = SkewSymmetricMatrix(t12);
cv::Mat K1 = this->toK();
@ -156,7 +259,19 @@ namespace ORB_SLAM3 {
return dsqr < 3.84 * unc;
}
bool Pinhole::epipolarConstrain_(GeometricCamera *pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Matx33f &R12, const cv::Matx31f &t12, const float sigmaLevel, const float unc) {
/**
* @brief 极线约束
* @param pCamera2 右相机
* @param kp1 左相机特征点
* @param kp2 右相机特征点
* @param R12 2->1的旋转
* @param t12 2->1的平移
* @param sigmaLevel 特征点1的尺度的平方
* @param unc 特征点2的尺度的平方
* @return 三维点恢复的成功与否
*/
bool Pinhole::epipolarConstrain_(GeometricCamera *pCamera2, const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Matx33f &R12, const cv::Matx31f &t12, const float sigmaLevel, const float unc)
{
//Compute Fundamental Matrix
auto t12x = SkewSymmetricMatrix_(t12);
auto K1 = this->toK_();
@ -180,22 +295,27 @@ namespace ORB_SLAM3 {
return dsqr < 3.84 * unc;
}
std::ostream & operator<<(std::ostream &os, const Pinhole &ph) {
std::ostream &operator<<(std::ostream &os, const Pinhole &ph)
{
os << ph.mvParameters[0] << " " << ph.mvParameters[1] << " " << ph.mvParameters[2] << " " << ph.mvParameters[3];
return os;
}
std::istream & operator>>(std::istream &is, Pinhole &ph) {
std::istream &operator>>(std::istream &is, Pinhole &ph)
{
float nextParam;
for(size_t i = 0; i < 4; i++){
for (size_t i = 0; i < 4; i++)
{
assert(is.good()); //Make sure the input stream is good
is >> nextParam;
ph.mvParameters[i] = nextParam;
}
return is;
}
/**
* @brief 反对称矩阵
*/
cv::Mat Pinhole::SkewSymmetricMatrix(const cv::Mat &v)
{
return (cv::Mat_<float>(3, 3) << 0, -v.at<float>(2), v.at<float>(1),
@ -203,6 +323,9 @@ namespace ORB_SLAM3 {
-v.at<float>(1), v.at<float>(0), 0);
}
/**
* @brief 反对称矩阵
*/
cv::Matx33f Pinhole::SkewSymmetricMatrix_(const cv::Matx31f &v)
{
cv::Matx33f skew{0.f, -v(2), v(1),

5
src/G2oTypes.cc
View File

@ -922,8 +922,13 @@ Eigen::Matrix3d Skew(const Eigen::Vector3d &w)
return W;
}
// BUG 应该改成svd.matrixV().transpose()
Eigen::Matrix3d NormalizeRotation(const Eigen::Matrix3d &R)
{
// 这里关注一下
// 1. 对于行列数一样的矩阵，Eigen::ComputeThinU | Eigen::ComputeThinV 与 Eigen::ComputeFullU | Eigen::ComputeFullV 一样
// 2. 对于行列数不同的矩阵，例如3*4 或者 4*3 矩阵只有3个奇异向量，计算的时候如果是Thin 那么得出的UV矩阵列数只能是3，如果是full那么就是4
// 3. thin会损失一部分数据，但是会加快计算，对于大型矩阵解算方程时，可以用thin加速得到结果
Eigen::JacobiSVD<Eigen::Matrix3d> svd(R,Eigen::ComputeFullU | Eigen::ComputeFullV);
return svd.matrixU()*svd.matrixV();
}