vins-mono后端优化

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我是靠谱客的博主跳跃薯片，最近开发中收集的这篇文章主要介绍vins-mono后端优化，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

预积分量约束第i，j帧的残差计算：

// 计算和给定相邻帧状态量的残差
    Eigen::Matrix<double, 15, 1> evaluate(const Eigen::Vector3d &Pi, const Eigen::Quaterniond &Qi, const Eigen::Vector3d &Vi, const Eigen::Vector3d &Bai, const Eigen::Vector3d &Bgi,
                                          const Eigen::Vector3d &Pj, const Eigen::Quaterniond &Qj, const Eigen::Vector3d &Vj, const Eigen::Vector3d &Baj, const Eigen::Vector3d &Bgj)
    {
        Eigen::Matrix<double, 15, 1> residuals;

        Eigen::Matrix3d dp_dba = jacobian.block<3, 3>(O_P, O_BA);
        Eigen::Matrix3d dp_dbg = jacobian.block<3, 3>(O_P, O_BG);

        Eigen::Matrix3d dq_dbg = jacobian.block<3, 3>(O_R, O_BG);

        Eigen::Matrix3d dv_dba = jacobian.block<3, 3>(O_V, O_BA);
        Eigen::Matrix3d dv_dbg = jacobian.block<3, 3>(O_V, O_BG);

        Eigen::Vector3d dba = Bai - linearized_ba;
        Eigen::Vector3d dbg = Bgi - linearized_bg;

        Eigen::Quaterniond corrected_delta_q = delta_q * Utility::deltaQ(dq_dbg * dbg);
        Eigen::Vector3d corrected_delta_v = delta_v + dv_dba * dba + dv_dbg * dbg;
        Eigen::Vector3d corrected_delta_p = delta_p + dp_dba * dba + dp_dbg * dbg;

        residuals.block<3, 1>(O_P, 0) = Qi.inverse() * (0.5 * G * sum_dt * sum_dt + Pj - Pi - Vi * sum_dt) - corrected_delta_p;
        residuals.block<3, 1>(O_R, 0) = 2 * (corrected_delta_q.inverse() * (Qi.inverse() * Qj)).vec();
        residuals.block<3, 1>(O_V, 0) = Qi.inverse() * (G * sum_dt + Vj - Vi) - corrected_delta_v;
        residuals.block<3, 1>(O_BA, 0) = Baj - Bai;
        residuals.block<3, 1>(O_BG, 0) = Bgj - Bgi;
        return residuals;
    }

由于更新bias会导致预积分量也发生变化，而重新积分非常复杂，所以对于预积分量直接在i时刻的bias附近用一阶泰勒展开来近似，而不用真的迭代计算。