编写一段集束优化的代码，数据集自己造

好的，这里提供一个简单的集束优化代码示例，数据集随机生成，适用于学习和理解集束优化过程。这里使用了Ceres Solver库进行优化。

首先，我们需要生成一些随机的位姿和地图点云数据，用于后面的集束优化。代码如下：

#include <iostream>
#include <vector>
#include <random>

#include <Eigen/Core>
#include <Eigen/Geometry>

struct Pose {
    Eigen::Quaterniond q;
    Eigen::Vector3d t;
};

struct Point {
    Eigen::Vector3d p;
};

std::vector<Pose> poses;
std::vector<Point> points;

void generateData() {
    std::default_random_engine engine(0);
    std::normal_distribution<double> noise(0.0, 0.1);

    // Generate 10 poses and 20 points
    for (int i = 0; i < 10; i++) {
        Eigen::Quaterniond q = Eigen::Quaterniond::UnitRandom();
        Eigen::Vector3d t = Eigen::Vector3d::Random();
        poses.push_back({q, t});
    }
    for (int i = 0; i < 20; i++) {
        Eigen::Vector3d p = Eigen::Vector3d::Random();
        points.push_back({p});
    }

    // Add noise to poses and points
    for (auto& pose : poses) {
        pose.q.coeffs() += noise(engine) * Eigen::Vector4d::Random();
        pose.t += noise(engine) * Eigen::Vector3d::Random();
    }
    for (auto& point : points) {
        point.p += noise(engine) * Eigen::Vector3d::Random();
    }
}

接下来，我们使用Ceres Solver进行集束优化。首先定义优化问题和误差项，代码如下：

#include <ceres/ceres.h>

struct ReprojectionError {
    ReprojectionError(Eigen::Vector2d observed, Eigen::Matrix3d K, Eigen::Vector3d p)
        : observed_(observed), K_(K), p_(p) {}

    template<typename T>
    bool operator()(const T* const pose, const T* const point, T* residuals) const {
        T R[3], t[3];
        for (int i = 0; i < 3; i++) {
            R[i] = pose[i];
            t[i] = pose[3 + i];
        }

        T X[3];
        ceres::AngleAxisRotatePoint(R, point, X);
        X[0] += t[0];
        X[1] += t[1];
        X[2] += t[2];

        T p[2];
        p[0] = T(K_(0, 0)) * X[0] / X[2] + T(K_(0, 2));
        p[1] = T(K_(1, 1)) * X[1] / X[2] + T(K_(1, 2));

        residuals[0] = p[0] - T(observed_(0));
        residuals[1] = p[1] - T(observed_(1));

        return true;
    }

    static ceres::CostFunction* create(Eigen::Vector2d observed, Eigen::Matrix3d K, Eigen::Vector3d p) {
        return new ceres::AutoDiffCostFunction<ReprojectionError, 2, 7, 3>(new ReprojectionError(observed, K, p));
    }

    Eigen::Vector2d observed_;
    Eigen::Matrix3d K_;
    Eigen::Vector3d p_;
};

void bundleAdjustment(const std::vector<Pose>& poses, const std::vector<Point>& points,
                      const std::vector<Eigen::Vector2d>& observed, const Eigen::Matrix3d& K) {
    ceres::Problem problem;

    // Add pose parameters
    for (int i = 0; i < poses.size(); i++) {
        auto& pose = poses[i];
        double* R_t = new double[7];
        R_t[0] = pose.q.x();
        R_t[1] = pose.q.y();
        R_t[2] = pose.q.z();
        R_t[3] = pose.q.w();
        R_t[4] = pose.t.x();
        R_t[5] = pose.t.y();
        R_t[6] = pose.t.z();
        problem.AddParameterBlock(R_t, 7);
    }

    // Add point parameters
    for (int i = 0; i < points.size(); i++) {
        auto& point = points[i];
        double* p = new double[3];
        p[0] = point.p.x();
        p[1] = point.p.y();
        p[2] = point.p.z();
        problem.AddParameterBlock(p, 3);
    }

    // Add reprojection error terms
    for (int i = 0; i < observed.size(); i++) {
        auto& obs = observed[i];
        auto& pose = poses[i / 2];
        auto& point = points[i % 2];
        ceres::CostFunction* cost_function = ReprojectionError::create(obs, K, point.p);
        problem.AddResidualBlock(cost_function, nullptr, &pose.q.x(), &point.p.x());
    }

    ceres::Solver::Options options;
    options.minimizer_progress_to_stdout = true;
    options.num_threads = 4;
    options.max_num_iterations = 100;

    ceres::Solver::Summary summary;
    ceres::Solve(options, &problem, &summary);

    std::cout << summary.BriefReport() << std::endl;
}

最后，我们在`main`函数中生成数据集，调用集束优化函数进行优化，代码如下：

int main() {
    generateData();

    Eigen::Matrix3d K;
    K << 500, 0, 320, 0, 500, 240, 0, 0, 1;

    std::vector<Eigen::Vector2d> observed;
    for (int i = 0; i < poses.size(); i++) {
        auto& pose = poses[i];
        for (int j = 0; j < points.size(); j++) {
            auto& point = points[j];
            Eigen::Vector3d X = pose.q * point.p + pose.t;
            Eigen::Vector3d x = K * X / X.z();
            x += Eigen::Vector3d::Random() * 2.0;
            observed.push_back(x.head<2>());
        }
    }

    bundleAdjustment(poses, points, observed, K);

    return 0;
}

这样就完成了一个简单的集束优化过程，可以通过调整代价函数、优化参数等来验证集束优化的效果。