首先通过vins_estimator mode监听几个Topic（频率2000Hz），将imu数据，feature数据，raw_image数据（用于回环检测）通过各自的回调函数封装起来

ros::Subscriber sub_imu = n.subscribe(IMU_TOPIC, 2000, imu_callback, ros::TransportHints().tcpNoDelay());ros::Subscriber sub_image = n.subscribe("/feature_tracker/feature", 2000, feature_callback);ros::Subscriber sub_raw_image = n.subscribe(IMAGE_TOPIC, 2000, raw_image_callback);

imu_buf.push(imu_msg);feature_buf.push(feature_msg);image_buf.push(make_pair(img_ptr->image, img_msg->header.stamp.toSec()));

然后开启处理measurement的线程

std::thread measurement_process{process};

process()函数中，首先将获取的传感器数据imu_buf feature_buf对齐，注意这里只保证了相邻的feature数据之间有完整的imu数据，并不能保证imu和feature数据的精确对齐

// multiple IMU measurements and only one vision(features) measurementsstd::vector
     
      
       
        , sensor_msgs::PointCloudConstPtr>>getMeasurements(){    std::vector
        
         
          
           , sensor_msgs::PointCloudConstPtr>> measurements; while (true) { if (imu_buf.empty() || feature_buf.empty()) return measurements; // synchronize, if strictly synchronize, should change to ">=" // end up with : imu_buf.front()->header.stamp < feature_buf.front()->header.stamp // 1. should have overlap if (!(imu_buf.back()->header.stamp > feature_buf.front()->header.stamp)) { ROS_WARN("wait for imu, only should happen at the beginning"); sum_of_wait++; return measurements; } // 2. should have complete imu measurements between two feature_buf msg if (!(imu_buf.front()->header.stamp < feature_buf.front()->header.stamp)) { ROS_WARN("throw img, only should happen at the beginning"); feature_buf.pop(); continue; } sensor_msgs::PointCloudConstPtr img_msg = feature_buf.front(); feature_buf.pop(); std::vector
           
             IMUs; while (imu_buf.front()->header.stamp <= img_msg->header.stamp) { IMUs.emplace_back(imu_buf.front()); imu_buf.pop(); } measurements.emplace_back(IMUs, img_msg); } return measurements;}
           
          
         
        
       
      
     

接下来进入对measurements数据的处理：

处理imu数据的接口函数是processIMU()

处理vision数据的借口函数是processImage()

（一）IMU

1. 核心API：

midPointIntegration(_dt, acc_0, gyr_0, _acc_1, _gyr_1, delta_p, delta_q, delta_v,                            linearized_ba, linearized_bg,                            result_delta_p, result_delta_q, result_delta_v,                            result_linearized_ba, result_linearized_bg, 1);

其中，0代表上次测量值，1代表当前测量值，delta_p，delta_q，delta_v代表相对预积分初始参考系的位移，旋转四元数，以及速度（例如，从k帧预积分到k+1帧，则参考系是k帧的imu坐标系）

对应实现的是公式：

相应的离散实现使用Euler，Mid-point，或者龙格库塔（RK4）数值积分方法。

Euler方法如下：

2. 求状态向量对bias的Jacobian，当bias变化较小时，使用Jacobian去更新状态；否则需要以当前imu为参考系，重新预积分，对应repropagation()。同时，需要计算error state model中误差传播方程的系数矩阵F和V：

// pre-integration     // time interval of two imu; last and current imu measurements; p,q,v relate to local frame; ba and bg; propagated p,q,v,ba,bg;     // whether to update Jacobian and calculate F,V    void midPointIntegration(double _dt,                             const Eigen::Vector3d &_acc_0, const Eigen::Vector3d &_gyr_0,                            const Eigen::Vector3d &_acc_1, const Eigen::Vector3d &_gyr_1,                            const Eigen::Vector3d &delta_p, const Eigen::Quaterniond &delta_q, const Eigen::Vector3d &delta_v,                            const Eigen::Vector3d &linearized_ba, const Eigen::Vector3d &linearized_bg,                            Eigen::Vector3d &result_delta_p, Eigen::Quaterniond &result_delta_q, Eigen::Vector3d &result_delta_v,                            Eigen::Vector3d &result_linearized_ba, Eigen::Vector3d &result_linearized_bg, bool update_jacobian)    {        //ROS_INFO("midpoint integration");        // mid-point integration with bias = 0        Vector3d un_acc_0 = delta_q * (_acc_0 - linearized_ba);         Vector3d un_gyr = 0.5 * (_gyr_0 + _gyr_1) - linearized_bg;        result_delta_q = delta_q * Quaterniond(1, un_gyr(0) * _dt / 2, un_gyr(1) * _dt / 2, un_gyr(2) * _dt / 2);        Vector3d un_acc_1 = result_delta_q * (_acc_1 - linearized_ba);        Vector3d un_acc = 0.5 * (un_acc_0 + un_acc_1);        result_delta_p = delta_p + delta_v * _dt + 0.5 * un_acc * _dt * _dt;        result_delta_v = delta_v + un_acc * _dt;        // ba and bg donot change        result_linearized_ba = linearized_ba;        result_linearized_bg = linearized_bg;        // jacobian to bias, used when the bias changes slightly and no need of repropagation        if(update_jacobian)        {            // same as un_gyr, gyrometer reference to the local frame bk            Vector3d w_x = 0.5 * (_gyr_0 + _gyr_1) - linearized_bg;                        // last acceleration measurement            Vector3d a_0_x = _acc_0 - linearized_ba;            // current acceleration measurement            Vector3d a_1_x = _acc_1 - linearized_ba;                        // used for cross-product            // pay attention to derivation of matrix product            Matrix3d R_w_x, R_a_0_x, R_a_1_x;            R_w_x<<0, -w_x(2), w_x(1),                w_x(2), 0, -w_x(0),                -w_x(1), w_x(0), 0;            R_a_0_x<<0, -a_0_x(2), a_0_x(1),                a_0_x(2), 0, -a_0_x(0),                -a_0_x(1), a_0_x(0), 0;            R_a_1_x<<0, -a_1_x(2), a_1_x(1),                a_1_x(2), 0, -a_1_x(0),                -a_1_x(1), a_1_x(0), 0;            // error state model            // should use discrete format and mid-point approximation            MatrixXd F = MatrixXd::Zero(15, 15);            F.block<3, 3>(0, 0) = Matrix3d::Identity();            F.block<3, 3>(0, 3) = -0.25 * delta_q.toRotationMatrix() * R_a_0_x * _dt * _dt +                                   -0.25 * result_delta_q.toRotationMatrix() * R_a_1_x * (Matrix3d::Identity() - R_w_x * _dt) * _dt * _dt;            F.block<3, 3>(0, 6) = MatrixXd::Identity(3,3) * _dt;            F.block<3, 3>(0, 9) = -0.25 * (delta_q.toRotationMatrix() + result_delta_q.toRotationMatrix()) * _dt * _dt;            F.block<3, 3>(0, 12) = -0.25 * result_delta_q.toRotationMatrix() * R_a_1_x * _dt * _dt * -_dt;            F.block<3, 3>(3, 3) = Matrix3d::Identity() - R_w_x * _dt;            F.block<3, 3>(3, 12) = -1.0 * MatrixXd::Identity(3,3) * _dt;            F.block<3, 3>(6, 3) = -0.5 * delta_q.toRotationMatrix() * R_a_0_x * _dt +                                   -0.5 * result_delta_q.toRotationMatrix() * R_a_1_x * (Matrix3d::Identity() - R_w_x * _dt) * _dt;            F.block<3, 3>(6, 6) = Matrix3d::Identity();            F.block<3, 3>(6, 9) = -0.5 * (delta_q.toRotationMatrix() + result_delta_q.toRotationMatrix()) * _dt;            F.block<3, 3>(6, 12) = -0.5 * result_delta_q.toRotationMatrix() * R_a_1_x * _dt * -_dt;            F.block<3, 3>(9, 9) = Matrix3d::Identity();            F.block<3, 3>(12, 12) = Matrix3d::Identity();            MatrixXd V = MatrixXd::Zero(15,18);            V.block<3, 3>(0, 0) =  0.25 * delta_q.toRotationMatrix() * _dt * _dt;            V.block<3, 3>(0, 3) =  0.25 * -result_delta_q.toRotationMatrix() * R_a_1_x  * _dt * _dt * 0.5 * _dt;            V.block<3, 3>(0, 6) =  0.25 * result_delta_q.toRotationMatrix() * _dt * _dt;            V.block<3, 3>(0, 9) =  V.block<3, 3>(0, 3);            V.block<3, 3>(3, 3) =  0.5 * MatrixXd::Identity(3,3) * _dt;            V.block<3, 3>(3, 9) =  0.5 * MatrixXd::Identity(3,3) * _dt;            V.block<3, 3>(6, 0) =  0.5 * delta_q.toRotationMatrix() * _dt;            V.block<3, 3>(6, 3) =  0.5 * -result_delta_q.toRotationMatrix() * R_a_1_x  * _dt * 0.5 * _dt;            V.block<3, 3>(6, 6) =  0.5 * result_delta_q.toRotationMatrix() * _dt;            V.block<3, 3>(6, 9) =  V.block<3, 3>(6, 3);            V.block<3, 3>(9, 12) = MatrixXd::Identity(3,3) * _dt;            V.block<3, 3>(12, 15) = MatrixXd::Identity(3,3) * _dt;            //step_jacobian = F;            //step_V = V;            jacobian = F * jacobian;            covariance = F * covariance * F.transpose() + V * noise * V.transpose();        }    }

 

 

（二）Vision

 首先判断该帧是否关键帧：

if (f_manager.addFeatureCheckParallax(frame_count, image))        marginalization_flag = MARGIN_OLD;    else        marginalization_flag = MARGIN_SECOND_NEW;

关键帧的判断依据是rotation-compensated过后的parallax足够大，并且tracking上的feature足够多；关键帧会保留在当前Sliding Window中，marginalize掉Sliding Window中最旧的状态，如果是非关键帧则优先marginalize掉。

1. 标定外参旋转矩阵

initial_ex_rotation.CalibrationExRotation(corres, pre_integrations[frame_count]->delta_q, calib_ric)

其中

pre_integrations[frame_count]->delta_q

是使用imu pre-integration获取的旋转矩阵，会和视觉跟踪求解fundamentalMatrix分解后获得的旋转矩阵构建约束方程，从而标定出外参旋转矩阵。

2. 线性初始化

if (solver_flag == INITIAL)    {        if (frame_count == WINDOW_SIZE)        {            bool result = false;            if( ESTIMATE_EXTRINSIC != 2 && (header.stamp.toSec() - initial_timestamp) > 0.1)            {               result = initialStructure();               initial_timestamp = header.stamp.toSec();            }            if(result)            {                solver_flag = NON_LINEAR;                solveOdometry();                slideWindow();                f_manager.removeFailures();                ROS_INFO("Initialization finish!");                last_R = Rs[WINDOW_SIZE];                last_P = Ps[WINDOW_SIZE];                last_R0 = Rs[0];                last_P0 = Ps[0];                            }            else                slideWindow();        }        else            frame_count++;    }

3. 非线性优化

else    {        TicToc t_solve;        solveOdometry();        ROS_DEBUG("solver costs: %fms", t_solve.toc());        if (failureDetection())        {            ROS_WARN("failure detection!");            failure_occur = 1;            clearState();            setParameter();            ROS_WARN("system reboot!");            return;        }        TicToc t_margin;        slideWindow();        f_manager.removeFailures();        ROS_DEBUG("marginalization costs: %fms", t_margin.toc());        // prepare output of VINS        key_poses.clear();        for (int i = 0; i <= WINDOW_SIZE; i++)            key_poses.push_back(Ps[i]);        last_R = Rs[WINDOW_SIZE];        last_P = Ps[WINDOW_SIZE];        last_R0 = Rs[0];        last_P0 = Ps[0];    }

 主要的初始化，非线性优化的api均在这里，因此放在后面去说明。