Marginalization and FEJ in Sliding Window VIO

[TOC]

Overview

滑动窗口与共视图

我们根据时空对 局部BA 进行分类：

时间：滑动窗口
空间：共视图

通过企业管理，解释滑窗的边缘化以及共视图为啥没有边缘化：

基于滑窗优化的边缘化，可以用公司小组成员离职类比，小组相当于滑窗，离职交接相当于保留共视信息，没有交接就回影响小组未来的发展；交接不好，重新搞；员工贡献不大直街裁掉，就是remove或throw；而基于共视图的，相当于组长从他的人脉网招人搭建队伍做事。

Eliminate Old Variables

Marginal Probability

\[\begin{aligned} P(\boldsymbol{a}, \boldsymbol{b}) &=\mathcal{N}\left(\left[\begin{array}{l} \boldsymbol{\mu}_{a} \\ \boldsymbol{\mu}_{b} \end{array}\right],\left[\begin{array}{ll} \boldsymbol{\Sigma}_{a a} & \boldsymbol{\Sigma}_{a b} \\ \boldsymbol{\Sigma}_{b a} & \boldsymbol{\Sigma}_{b b} \end{array}\right]\right) \\ &=\mathcal{N}^{-1}\left(\left[\begin{array}{l} \boldsymbol{\eta}_{a} \\ \boldsymbol{\eta}_{b} \end{array}\right],\left[\begin{array}{cc} \boldsymbol{\Lambda}_{a a} & \boldsymbol{\Lambda}_{a b} \\ \boldsymbol{\Lambda}_{b a} & \boldsymbol{\Lambda}_{b b} \end{array}\right]\right) \end{aligned}\]

Marginalization and Conditioning operations on a gaussian distribution expresssed in convariance and information form.

Three Math Methods for Marginalization

Throwing Rows and Cols with Covariance Matrix (Filter-based)
Nullspace Projection with Jacobian Matrix (MSCKF)
- SVD
- QR
Schur Complement with Hessian/Information Matrix (Optimization-based Sliding window)

舒尔补 (Schur Complement)

\[M= \left[\begin{array}{ll} A & B \\ C & D \end{array}\right]\]

则 D在M中的舒尔补 为

\[M / D := A-B D^{-1} C\]

在矩阵方程求解中的应用

线性方程组

\[\begin{aligned} &A x+B y=a \\ &C x+D y=b \end{aligned}\]

利用 D的舒尔补先求 $x$

\[\left(A-B D^{-1} C\right) x=a-B D^{-1} b\]

解出未知量 $x$ 之后带入第二个方程 $C x+D y=b$ 就可以解出 $y$

Marginalization in VINS-Mono

Two way marginalization

当滑动窗口中第二新的图像帧为关键帧，则 marg 最老的帧,以及上面的路标点;
当滑动窗口中第二新的图像帧不是关键帧,则丢弃这一帧上的视觉测量信息，IMU 预积分传给下一帧。

数据结构

代码逻辑

addResidualBlockInfo

factors.emplace_back(residual_block_info);

std::vector<int> &drop_set = residual_block_info->drop_set;
std::vector<double *> &parameter_blocks = residual_block_info->parameter_blocks;

for (int i = 0; i < parameter_blocks.size(); i++) {
    long addr = reinterpret_cast<long>(parameter_blocks[i]);
    parameter_block_size[addr] =
        residual_block_info->cost_function->parameter_block_sizes()[i];
}

for (int i = 0; i < drop_set.size(); i++) {
    long addr = reinterpret_cast<long>(parameter_blocks[drop_set[i]]);
    parameter_block_idx[addr] = 0;
}

preMarginalize

for (auto it : factors) {
    it->Evaluate(); // 利用多态性分别计算所有状态变量构成的残差和雅克比矩阵

    std::vector<int> block_sizes = it->cost_function->parameter_block_sizes();

    for (int i = 0; i < static_cast<int>(block_sizes.size()); i++) {
        int size = block_sizes[i];
        long addr = reinterpret_cast<long>(it->parameter_blocks[i]);
        if (parameter_block_data.find(addr) == parameter_block_data.end()) {
            double \*data = new double[size];
            memcpy(data, it->parameter_blocks[i], sizeof(double) * size);
            parameter_block_data[addr] = data;
        }
    }
}

marginalize

Marginalization via Schur complement on information matrix

fill in of the information matrix

linearized_jacobians & linearized_residuals

A = Arr - Arm * Amm_inv * Amr;
b = brr - Arm * Amm_inv * bmm;

Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> saes2(A);
Eigen::VectorXd S     = Eigen::VectorXd((saes2.eigenvalues().array() > eps).select(saes2.eigenvalues().array(), 0));
Eigen::VectorXd S_inv = Eigen::VectorXd((saes2.eigenvalues().array() > eps).select(saes2.eigenvalues().array().inverse(), 0));

Eigen::VectorXd S_sqrt     =     S.cwiseSqrt();
Eigen::VectorXd S_inv_sqrt = S_inv.cwiseSqrt();

linearized_jacobians =     S_sqrt.asDiagonal() * saes2.eigenvectors().transpose();
linearized_residuals = S_inv_sqrt.asDiagonal() * saes2.eigenvectors().transpose() * b;

MarginalizationFactor::Evaluate

int n = marginalization_info->n;
int m = marginalization_info->m;

Eigen::VectorXd dx(n);
for (int i = 0; i < static_cast<int>(marginalization_info->keep_block_size.size()); i++) {
    int size = marginalization_info->keep_block_size[i];
    int idx  = marginalization_info->keep_block_idx[i] - m;
    Eigen::VectorXd x  = Eigen::Map<const Eigen::VectorXd>(parameters[i], size);
    Eigen::VectorXd x0 = Eigen::Map<const Eigen::VectorXd>(marginalization_info->keep_block_data[i], size);
    if (size != 7)
        dx.segment(idx, size) = x - x0;
    else {
        Eigen::Quaterniond q_tmp =
            Eigen::Quaterniond(x0(6), x0(3), x0(4), x0(5)).inverse() * Eigen::Quaterniond(x(6), x(3), x(4), x(5));
        dx.segment<3>(idx + 0) = x.head<3>() - x0.head<3>();
        dx.segment<3>(idx + 3) = 2.0 * Utility::positify(q_tmp).vec();
        if (!(q_tmp.w() >= 0)) {
            dx.segment<3>(idx + 3) = 2.0 * -Utility::positify(q_tmp).vec();
        }
    }
}

Eigen::Map<Eigen::VectorXd>(residuals, n) =
    marginalization_info->linearized_residuals + marginalization_info->linearized_jacobians * dx;

if (jacobians) {
    for (int i = 0; i < static_cast<int>(marginalization_info->keep_block_size.size()); i++) {
        if (jacobians[i]) {
            int size = marginalization_info->keep_block_size[i];
            int local_size = marginalization_info->localSize(size);
            int idx = marginalization_info->keep_block_idx[i] - m;
            Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> jacobian(jacobians[i], n, size);
            jacobian.setZero();
            jacobian.leftCols(local_size) = marginalization_info->linearized_jacobians.middleCols(idx, local_size);
        }
    }
}