• 视觉十四讲:第十讲_位姿图


    1.简介

    带有相机位姿和空间点的图优化称为BA,能够有效的求解大范围的定位与建图问题,但是随着时间,规模越来越大,计算效率会大幅下降。我们发现,特征点在优化问题中占了很大部分,经过若干次迭代之后,特征点就会收敛,此时再进行优化的意义并不大,因此,在优化几次后,可以把特征点固定住,把他们看做位姿估计的约束,不再优化他们的位姿。
    位姿图即只考虑位姿,构建一个只有轨迹的图优化,而位姿节点之间的边,由两个关键帧之间通过特征匹配后得到的运动估计来给定初始值,一旦初始值完成,就不再优化路标点的位置,只关心相机位姿之间的联系。

    2.位姿优化:

    图优化中的节点表示相机位姿,以(xi_{1}, ..., xi_{n})来表示。边,则是两个位姿节点之间的相对运动估计。
    (xi_{i})(xi_{j})之间的一个运动(Delta xi_{ij})

    按李群写法:
    构建误差(e_{ij})

    注意优化变量有两个:(xi_{i})(xi_{j}),因此求(e_{ij})关于这两个变量的导数,按照李代数的求导方式,给两个优化变量各一个左扰动,于是误差变为:
    把扰动项移至式子的左侧或右侧,有:

    因此,按照李代数上的求导法则,我们求出来误差关于两个位姿的雅可比矩阵。

    关于Ti的:

    关于Tj的:

    由于雅可比矩阵Jr形式过于复杂,我们通常取它的近似。如果误差接近于零,我们设它近似于I或:

    得到雅可比矩阵后,剩下的部分就和普通的图优化一样了,所有的位姿顶点和位姿边构成了一个图优化,本质上是一个最小二乘问题,优化变量为各个顶点的位姿,边来自于位姿观测约束。则总体的目标函数为:

    3.实践

    ++ g2o自带的数据类型,默认使用四元数和平移向量表达位姿。
    顶点:(ID, t_{x}, t_{y}, t_{z}, q_{x}, q_{y}, q_{z}, q_{w})。前面为平移向量,后面为旋转的单位四元数。
    边:(两个节点的ID, t_{x}, t_{y}, t_{z}, q_{x}, q_{y}, q_{z}, q_{w}),信息矩阵的右上角
    (信息矩阵为对称矩阵,只保留一半即可)。

    #include <iostream>
    #include <fstream>
    #include <string>
    
    #include <g2o/types/slam3d/types_slam3d.h>
    #include <g2o/core/block_solver.h>
    #include <g2o/core/optimization_algorithm_levenberg.h>
    #include <g2o/solvers/eigen/linear_solver_eigen.h>
    
    using namespace std;
    
    int main(int argc, char **argv) {
        if (argc != 2) {
            cout << "Usage: pose_graph_g2o_SE3 sphere.g2o" << endl;
            return 1;
        }
        ifstream fin(argv[1]);
        if (!fin) {
            cout << "file " << argv[1] << " does not exist." << endl;
            return 1;
        }
    
        // 设定g2o
        typedef g2o::BlockSolver<g2o::BlockSolverTraits<6, 6>> BlockSolverType;
        typedef g2o::LinearSolverEigen<BlockSolverType::PoseMatrixType> LinearSolverType;
        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
    
        int vertexCnt = 0, edgeCnt = 0; // 顶点和边的数量
        while (!fin.eof()) {
            string name;
            fin >> name;
            if (name == "VERTEX_SE3:QUAT") {
                // SE3 顶点
                g2o::VertexSE3 *v = new g2o::VertexSE3();
                int index = 0;
                fin >> index;
                v->setId(index);
                v->read(fin);
                optimizer.addVertex(v);
                vertexCnt++;
                if (index == 0)
                    v->setFixed(true);
            } else if (name == "EDGE_SE3:QUAT") {
                // SE3-SE3 边
                g2o::EdgeSE3 *e = new g2o::EdgeSE3();
                int idx1, idx2;     // 关联的两个顶点
                fin >> idx1 >> idx2;
                e->setId(edgeCnt++);
                e->setVertex(0, optimizer.vertices()[idx1]);
                e->setVertex(1, optimizer.vertices()[idx2]);
                e->read(fin);
                optimizer.addEdge(e);
            }
            if (!fin.good()) break;
        }
    
        cout << "read total " << vertexCnt << " vertices, " << edgeCnt << " edges." << endl;
    
        cout << "optimizing ..." << endl;
        optimizer.initializeOptimization();
        optimizer.optimize(30);
    
        cout << "saving optimization results ..." << endl;
        optimizer.save("result.g2o");
    
        return 0;
    }
    

    ++ 自定义李代数数据类型

    #include <iostream>
    #include <fstream>
    #include <string>
    #include <Eigen/Core>
    
    #include <g2o/core/base_vertex.h>
    #include <g2o/core/base_binary_edge.h>
    #include <g2o/core/block_solver.h>
    #include <g2o/core/optimization_algorithm_levenberg.h>
    #include <g2o/solvers/eigen/linear_solver_eigen.h>
    
    #include <sophus/se3.hpp>
    
    using namespace std;
    using namespace Eigen;
    using Sophus::SE3d;
    using Sophus::SO3d;
    
    typedef Matrix<double, 6, 6> Matrix6d;
    
    // 给定误差求J_R^{-1}的近似
    Matrix6d JRInv(const SE3d &e) {
        Matrix6d J;
        J.block(0, 0, 3, 3) = SO3d::hat(e.so3().log());
        J.block(0, 3, 3, 3) = SO3d::hat(e.translation());
        J.block(3, 0, 3, 3) = Matrix3d::Zero(3, 3);
        J.block(3, 3, 3, 3) = SO3d::hat(e.so3().log());
        // J = J * 0.5 + Matrix6d::Identity();
        J = Matrix6d::Identity();    // try Identity if you want
        return J;
    }
    
    // 李代数顶点
    typedef Matrix<double, 6, 1> Vector6d;
    
    class VertexSE3LieAlgebra : public g2o::BaseVertex<6, SE3d> {
    public:
        EIGEN_MAKE_ALIGNED_OPERATOR_NEW
    
        virtual bool read(istream &is) override {
            double data[7];
            for (int i = 0; i < 7; i++)
                is >> data[i];
            setEstimate(SE3d(
                Quaterniond(data[6], data[3], data[4], data[5]),
                Vector3d(data[0], data[1], data[2])
            ));
        }
    
        virtual bool write(ostream &os) const override {
            os << id() << " ";
            Quaterniond q = _estimate.unit_quaternion();
            os << _estimate.translation().transpose() << " ";
            os << q.coeffs()[0] << " " << q.coeffs()[1] << " " << q.coeffs()[2] << " " << q.coeffs()[3] << endl;
            return true;
        }
    
        virtual void setToOriginImpl() override {
            _estimate = SE3d();
        }
    
        // 左乘更新
        virtual void oplusImpl(const double *update) override {
            Vector6d upd;
            upd << update[0], update[1], update[2], update[3], update[4], update[5];
            _estimate = SE3d::exp(upd) * _estimate;
        }
    };
    
    // 两个李代数节点之边
    class EdgeSE3LieAlgebra : public g2o::BaseBinaryEdge<6, SE3d, VertexSE3LieAlgebra, VertexSE3LieAlgebra> {
    public:
        EIGEN_MAKE_ALIGNED_OPERATOR_NEW
    
        virtual bool read(istream &is) override {
            double data[7];
            for (int i = 0; i < 7; i++)
                is >> data[i];
            Quaterniond q(data[6], data[3], data[4], data[5]);
            q.normalize();
            setMeasurement(SE3d(q, Vector3d(data[0], data[1], data[2])));
            for (int i = 0; i < information().rows() && is.good(); i++)
                for (int j = i; j < information().cols() && is.good(); j++) {
                    is >> information()(i, j);
                    if (i != j)
                        information()(j, i) = information()(i, j);
                }
            return true;
        }
    
        virtual bool write(ostream &os) const override {
            VertexSE3LieAlgebra *v1 = static_cast<VertexSE3LieAlgebra *> (_vertices[0]);
            VertexSE3LieAlgebra *v2 = static_cast<VertexSE3LieAlgebra *> (_vertices[1]);
            os << v1->id() << " " << v2->id() << " ";
            SE3d m = _measurement;
            Eigen::Quaterniond q = m.unit_quaternion();
            os << m.translation().transpose() << " ";
            os << q.coeffs()[0] << " " << q.coeffs()[1] << " " << q.coeffs()[2] << " " << q.coeffs()[3] << " ";
    
            // information matrix 
            for (int i = 0; i < information().rows(); i++)
                for (int j = i; j < information().cols(); j++) {
                    os << information()(i, j) << " ";
                }
            os << endl;
            return true;
        }
    
        // 误差计算与书中推导一致
        virtual void computeError() override {
            SE3d v1 = (static_cast<VertexSE3LieAlgebra *> (_vertices[0]))->estimate();
            SE3d v2 = (static_cast<VertexSE3LieAlgebra *> (_vertices[1]))->estimate();
            _error = (_measurement.inverse() * v1.inverse() * v2).log();
        }
    
        // 雅可比计算
        virtual void linearizeOplus() override {
            SE3d v1 = (static_cast<VertexSE3LieAlgebra *> (_vertices[0]))->estimate();
            SE3d v2 = (static_cast<VertexSE3LieAlgebra *> (_vertices[1]))->estimate();
            Matrix6d J = JRInv(SE3d::exp(_error));
            // 尝试把J近似为I?
            _jacobianOplusXi = -J * v2.inverse().Adj();
            _jacobianOplusXj = J * v2.inverse().Adj();
        }
    };
    
    int main(int argc, char **argv) {
        if (argc != 2) {
            cout << "Usage: pose_graph_g2o_SE3_lie sphere.g2o" << endl;
            return 1;
        }
        ifstream fin(argv[1]);
        if (!fin) {
            cout << "file " << argv[1] << " does not exist." << endl;
            return 1;
        }
    
        // 设定g2o
        typedef g2o::BlockSolver<g2o::BlockSolverTraits<6, 6>> BlockSolverType;
        typedef g2o::LinearSolverEigen<BlockSolverType::PoseMatrixType> LinearSolverType;
        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
    
        int vertexCnt = 0, edgeCnt = 0; // 顶点和边的数量
    
        vector<VertexSE3LieAlgebra *> vectices;
        vector<EdgeSE3LieAlgebra *> edges;
        while (!fin.eof()) {
            string name;
            fin >> name;
            if (name == "VERTEX_SE3:QUAT") {
                // 顶点
                VertexSE3LieAlgebra *v = new VertexSE3LieAlgebra();
                int index = 0;
                fin >> index;
                v->setId(index);
                v->read(fin);
                optimizer.addVertex(v);
                vertexCnt++;
                vectices.push_back(v);
                if (index == 0)
                    v->setFixed(true);
            } else if (name == "EDGE_SE3:QUAT") {
                // SE3-SE3 边
                EdgeSE3LieAlgebra *e = new EdgeSE3LieAlgebra();
                int idx1, idx2;     // 关联的两个顶点
                fin >> idx1 >> idx2;
                e->setId(edgeCnt++);
                e->setVertex(0, optimizer.vertices()[idx1]);
                e->setVertex(1, optimizer.vertices()[idx2]);
                e->read(fin);
                optimizer.addEdge(e);
                edges.push_back(e);
            }
            if (!fin.good()) break;
        }
    
        cout << "read total " << vertexCnt << " vertices, " << edgeCnt << " edges." << endl;
    
        cout << "optimizing ..." << endl;
        optimizer.initializeOptimization();
        optimizer.optimize(30);
    
        cout << "saving optimization results ..." << endl;
    
        // 因为用了自定义顶点且没有向g2o注册,这里保存自己来实现
        // 伪装成 SE3 顶点和边,让 g2o_viewer 可以认出
        ofstream fout("result_lie.g2o");
        for (VertexSE3LieAlgebra *v:vectices) {
            fout << "VERTEX_SE3:QUAT ";
            v->write(fout);
        }
        for (EdgeSE3LieAlgebra *e:edges) {
            fout << "EDGE_SE3:QUAT ";
            e->write(fout);
        }
        fout.close();
        return 0;
    }
    
    

    CMakeLists.txt

    cmake_minimum_required(VERSION 2.8)
    project(pose_graph)
    
    set(CMAKE_BUILD_TYPE "Release")
    set(CMAKE_CXX_FLAGS "-std=c++11 -O2")
    
    list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules)
    
    # Eigen
    include_directories("/usr/include/eigen3")
    
    # sophus 
    find_package(Sophus REQUIRED)
    include_directories(${Sophus_INCLUDE_DIRS})
    
    # g2o 
    find_package(G2O REQUIRED)
    include_directories(${G2O_INCLUDE_DIRS})
    
    add_executable(pose_graph_g2o_SE3 pose_graph_g2o_SE3.cpp)
    target_link_libraries(pose_graph_g2o_SE3
            g2o_core g2o_stuff g2o_types_slam3d ${CHOLMOD_LIBRARIES}
            )
    
    add_executable(pose_graph_g2o_lie pose_graph_g2o_lie_algebra.cpp)
    target_link_libraries(pose_graph_g2o_lie
            g2o_core g2o_stuff
            ${CHOLMOD_LIBRARIES}
            ${Sophus_LIBRARIES}
            )
    
    
    
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  • 原文地址:https://www.cnblogs.com/penuel/p/13631133.html
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