93 lines
3.6 KiB
C++
93 lines
3.6 KiB
C++
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/**
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* This file is part of ORB-SLAM3
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*
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* Copyright (C) 2017-2021 Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
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* Copyright (C) 2014-2016 Raúl Mur-Artal, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
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*
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* ORB-SLAM3 is free software: you can redistribute it and/or modify it under the terms of the GNU General Public
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* License as published by the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* ORB-SLAM3 is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even
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* the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License along with ORB-SLAM3.
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* If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "GeometricTools.h"
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#include "KeyFrame.h"
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namespace ORB_SLAM3
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{
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/**
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* @brief 计算两个关键帧间的基础矩阵
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* @param pKF1 关键帧1
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* @param pKF2 关键帧2
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*/
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Eigen::Matrix3f GeometricTools::ComputeF12(KeyFrame* &pKF1, KeyFrame* &pKF2)
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{
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// 获取关键帧1的旋转平移
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Sophus::SE3<float> Tc1w = pKF1->GetPose();
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Sophus::Matrix3<float> Rc1w = Tc1w.rotationMatrix();
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Sophus::SE3<float>::TranslationMember tc1w = Tc1w.translation();
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// 获取关键帧2的旋转平移
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Sophus::SE3<float> Tc2w = pKF2->GetPose();
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Sophus::Matrix3<float> Rc2w = Tc2w.rotationMatrix();
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Sophus::SE3<float>::TranslationMember tc2w = Tc2w.translation();
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// 计算2->1的旋转平移
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Sophus::Matrix3<float> Rc1c2 = Rc1w * Rc2w.transpose();
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Eigen::Vector3f tc1c2 = -Rc1c2 * tc2w + tc1w;
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Eigen::Matrix3f tc1c2x = Sophus::SO3f::hat(tc1c2);
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const Eigen::Matrix3f K1 = pKF1->mpCamera->toK_();
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const Eigen::Matrix3f K2 = pKF2->mpCamera->toK_();
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return K1.transpose().inverse() * tc1c2x * Rc1c2 * K2.inverse();
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}
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/**
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* @brief 三角化获得三维点
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* @param x_c1 点在关键帧1下的归一化坐标
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* @param x_c2 点在关键帧2下的归一化坐标
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* @param Tc1w 关键帧1投影矩阵 [K*R | K*t]
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* @param Tc2w 关键帧2投影矩阵 [K*R | K*t]
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* @param x3D 三维点坐标，作为结果输出
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*/
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bool GeometricTools::Triangulate(
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Eigen::Vector3f &x_c1, Eigen::Vector3f &x_c2, Eigen::Matrix<float,3,4> &Tc1w, Eigen::Matrix<float,3,4> &Tc2w,
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Eigen::Vector3f &x3D)
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{
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Eigen::Matrix4f A;
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// x = a*P*X， 左右两面乘Pc的反对称矩阵 a*[x]^ * P *X = 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
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// 0 -1 v P(0) -P.row(1) + v*P.row(2)
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// 1 0 -u * P(1) = P.row(0) - u*P.row(2)
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// -v u 0 P(2) u*P.row(1) - v*P.row(0)
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// 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
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A.block<1,4>(0,0) = x_c1(0) * Tc1w.block<1,4>(2,0) - Tc1w.block<1,4>(0,0);
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A.block<1,4>(1,0) = x_c1(1) * Tc1w.block<1,4>(2,0) - Tc1w.block<1,4>(1,0);
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A.block<1,4>(2,0) = x_c2(0) * Tc2w.block<1,4>(2,0) - Tc2w.block<1,4>(0,0);
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A.block<1,4>(3,0) = x_c2(1) * Tc2w.block<1,4>(2,0) - Tc2w.block<1,4>(1,0);
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// 解方程 AX=0
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Eigen::JacobiSVD<Eigen::Matrix4f> svd(A, Eigen::ComputeFullV);
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Eigen::Vector4f x3Dh = svd.matrixV().col(3);
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if(x3Dh(3)==0)
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return false;
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// Euclidean coordinates
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x3D = x3Dh.head(3)/x3Dh(3);
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return true;
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}
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} //namespace ORB_SLAM
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