ORBSLAM3轨迹对齐C++代码

ORBSLAM3是一个视觉SLAM系统，它可以在没有先验地图的情况下从单目、双目和RGB-D相机中提取出环境的三维信息。在ORBSLAM3中，轨迹对齐是一个重要的步骤，它可以将多个视觉SLAM系统的轨迹对齐到同一个坐标系中。以下是ORBSLAM3轨迹对齐的C++代码：

void Optimizer::BundleAdjustment(const vector<Frame>& vFrames, const vector<MapPoint*>& vpMapPoints, bool* pbStopFlag, const unsigned long nLoopKF, const bool bRobust)
{
    // Create optimization problem
    ceres::Problem problem;
    ceres::LossFunction* loss_function = bRobust ? new ceres::HuberLoss(2.0) : NULL;

    // Add pose blocks
    for(size_t i=0; i<vFrames.size(); i++)
    {
        if(vFrames[i].isBad())
            continue;

        const cv::Mat Rcw = vFrames[i].GetRotation();
        const cv::Mat tcw = vFrames[i].GetTranslation();

        // Global pose optimization is disabled for the first and second frames
        if(vFrames[i].mnId != 0 && vFrames[i].mnId != 1)
        {
            double angleAxis[3];
            ceres::RotationMatrixToAngleAxis((const double*)Rcw.data, angleAxis);
            problem.AddParameterBlock(angleAxis, 3, new ceres::EigenQuaternionParameterization());
            problem.AddParameterBlock((double*)tcw.data, 3);
            if(i==0)
            {
                problem.SetParameterBlockConstant(angleAxis);
                problem.SetParameterBlockConstant((double*)tcw.data);
            }
            else
            {
                if(vFrames[i].mnId == nLoopKF)
                {
                    ceres::LocalParameterization* quaternion_local_parameterization = new ceres::QuaternionParameterization;
                    problem.SetParameterization(angleAxis, quaternion_local_parameterization);
                }
            }
        }
    }

    // Add point blocks
    for(size_t i=0; i<vpMapPoints.size(); i++)
    {
        MapPoint* pMP = vpMapPoints[i];
        if(pMP==NULL || pMP->isBad())
            continue;

        ceres::CostFunction* cost_function = ReprojectionError::Create(pMP->GetObservationList(), pMP->GetWorldPos());

        vector<Frame*> vObservingFrames = pMP->GetObservingFrames();

        for(size_t j=0; j<vObservingFrames.size(); j++)
        {
            Frame* pFrame = vObservingFrames[j];
            if(pFrame->isBad())
                continue;

            ceres::LossFunction* frame_loss_function = vObservingFrames[j]->mnId == nLoopKF ? loss_function : NULL;

            double angleAxis[3];
            ceres::RotationMatrixToAngleAxis((const double*)(pFrame->GetRotation().data), angleAxis);

            problem.AddResidualBlock(cost_function, frame_loss_function, angleAxis, (double*)(pFrame->GetTranslation().data), pMP->GetWorldPos());
        }
    }

    // Set solver options
    ceres::Solver::Options options;
    options.linear_solver_type = ceres::DENSE_SCHUR;
    options.max_num_iterations = 40;
    options.num_threads = 4;
    options.trust_region_strategy_type = ceres::DOGLEG;
    options.minimizer_progress_to_stdout = false;
    options.check_gradients = false;
    options.gradient_check_relative_precision = 1e-4;
    options.gradient_check_numeric_derivative_relative_step_size = 1e-5;

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

    if(pbStopFlag)
        if(*pbStopFlag)
            return;

    // Update keyframe poses
    for(size_t i=0; i<vFrames.size(); i++)
    {
        if(vFrames[i].isBad())
            continue;

        double angleAxis[3];
        if(vFrames[i].mnId != 0 && vFrames[i].mnId != 1)
        {
            ceres::AngleAxisToQuaternion((const double*)vFrames[i].GetRotation().data, angleAxis);
            Eigen::Quaterniond q = Eigen::Map<Eigen::Quaterniond>(angleAxis);
            cv::Mat Rcw(q.toRotationMatrix());
            cv::Mat tcw = cv::Mat(3,1,CV_64F,vFrames[i].GetTranslation().data);
            vFrames[i].SetPose(Rcw,tcw);
        }
    }

    // Update map points
    for(size_t i=0; i<vpMapPoints.size(); i++)
    {
        MapPoint* pMP = vpMapPoints[i];
        if(pMP==NULL || pMP->isBad())
            continue;

        pMP->UpdateNormalAndDepth();
    }
}

该代码使用Ceres进行优化，将视觉SLAM系统的所有帧和地图点作为优化问题的参数，使用重投影误差作为代价函数，最小化重投影误差，从而优化相机位姿和地图点位置。该代码还包括了一些优化器参数和选项，如线性求解器类型、最大迭代次数、线程数等。