ORBSLAM输出的轨迹与数据集真实轨迹进行空间轨迹对齐并输出txt文件C++代码

以下是一个简单的C++代码示例，用于将ORBSLAM输出的轨迹与真实轨迹进行空间轨迹对齐并输出txt文件。这里假设你已经有了ORBSLAM输出的轨迹文件和真实轨迹文件，每个文件中包含了时间戳、位置和姿态信息。

首先，我们需要读取这两个文件并将它们存储到两个向量中：

#include <iostream>
#include <fstream>
#include <vector>
#include <string>

struct Pose {
    double timestamp;
    double x, y, z;
    double qw, qx, qy, qz;
};

std::vector<Pose> read_trajectory(const std::string& filename) {
    std::ifstream file(filename);
    if (!file) {
        std::cerr << "Cannot open file " << filename << std::endl;
        exit(EXIT_FAILURE);
    }

    std::vector<Pose> trajectory;
    std::string line;
    while (std::getline(file, line)) {
        std::istringstream iss(line);
        Pose pose;
        iss >> pose.timestamp >> pose.x >> pose.y >> pose.z
            >> pose.qw >> pose.qx >> pose.qy >> pose.qz;
        trajectory.push_back(pose);
    }
    return trajectory;
}

int main(int argc, char** argv) {
    std::string orbslam_file = "ORBSLAM_trajectory.txt";
    std::string ground_truth_file = "ground_truth_trajectory.txt";

    auto orbslam_trajectory = read_trajectory(orbslam_file);
    auto ground_truth_trajectory = read_trajectory(ground_truth_file);

    // ...
}

接下来，我们需要对ORBSLAM输出的轨迹进行空间对齐。这可以通过计算平移和旋转矩阵来实现。我们假设ORBSLAM输出的轨迹是相对于某个初始位置和方向的，而真实轨迹是相对于全局坐标系的。因此，我们需要找到一个平移和旋转矩阵，将ORBSLAM输出的轨迹从初始坐标系变换到全局坐标系：

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

Eigen::MatrixXd to_matrix(const Pose& pose) {
    Eigen::Quaterniond q(pose.qw, pose.qx, pose.qy, pose.qz);
    Eigen::Matrix3d R = q.normalized().toRotationMatrix();
    Eigen::MatrixXd T(4, 4);
    T << R(0,0), R(0,1), R(0,2), pose.x,
         R(1,0), R(1,1), R(1,2), pose.y,
         R(2,0), R(2,1), R(2,2), pose.z,
         0,      0,      0,      1;
    return T;
}

Eigen::Matrix4d find_transform(const std::vector<Pose>& src_trajectory,
                               const std::vector<Pose>& dst_trajectory) {
    Eigen::MatrixXd A(4*src_trajectory.size(), 12);
    Eigen::MatrixXd B(4*src_trajectory.size(), 1);

    for (int i = 0; i < src_trajectory.size(); i++) {
        auto src_pose = src_trajectory[i];
        auto dst_pose = dst_trajectory[i];

        Eigen::MatrixXd T_src = to_matrix(src_pose);
        Eigen::MatrixXd T_dst = to_matrix(dst_pose);

        A.block<4, 3>(4*i, 0) = T_src.block<4, 3>(0, 0);
        A.block<4, 3>(4*i, 3) = Eigen::MatrixXd::Identity(4, 3);
        B.block<4, 1>(4*i, 0) = T_dst.col(3);
    }

    Eigen::MatrixXd x = (A.transpose() * A).inverse() * A.transpose() * B;
    Eigen::Matrix4d transform;
    transform << x(0,0), x(1,0), x(2,0), x(3,0),
                 x(4,0), x(5,0), x(6,0), x(7,0),
                 x(8,0), x(9,0), x(10,0), x(11,0),
                 0,      0,      0,       1;
    return transform;
}

auto transform = find_transform(orbslam_trajectory, ground_truth_trajectory);

现在，我们有了一个变换矩阵，可以将ORBSLAM输出的轨迹从初始坐标系变换到全局坐标系。我们可以使用这个变换矩阵，将ORBSLAM输出的每个位姿变换到全局坐标系中，并将结果写入一个新的txt文件：

std::ofstream outfile("aligned_trajectory.txt");

for (int i = 0; i < orbslam_trajectory.size(); i++) {
    auto pose = orbslam_trajectory[i];
    Eigen::MatrixXd T = to_matrix(pose);
    Eigen::MatrixXd T_aligned = transform * T;
    Pose pose_aligned;
    pose_aligned.timestamp = pose.timestamp;
    pose_aligned.x = T_aligned(0, 3);
    pose_aligned.y = T_aligned(1, 3);
    pose_aligned.z = T_aligned(2, 3);
    Eigen::Quaterniond q(T_aligned.block<3, 3>(0, 0));
    pose_aligned.qw = q.w();
    pose_aligned.qx = q.x();
    pose_aligned.qy = q.y();
    pose_aligned.qz = q.z();
    outfile << pose_aligned.timestamp << " "
            << pose_aligned.x << " "
            << pose_aligned.y << " "
            << pose_aligned.z << " "
            << pose_aligned.qw << " "
            << pose_aligned.qx << " "
            << pose_aligned.qy << " "
            << pose_aligned.qz << std::endl;
}

outfile.close();

现在，我们已经将ORBSLAM输出的轨迹与真实轨迹进行了空间轨迹对齐，并将结果输出到了一个新的txt文件中。