论文标题
对于平面姿势图，全球离群拒绝的脱钩和线性框架
A Decoupled and Linear Framework for Global Outlier Rejection over Planar Pose Graph
论文作者
论文摘要
我们为平面姿势图优化提供了一个可靠的框架，该框架被环闭合离群值污染。我们的框架首先将截短的最小二乘内核包裹的强大的PGO问题拒绝了异常值，从而拒绝了异常值。然后，该框架引入了线性角度表示，以重写最初用旋转矩阵配制的第一个子问题。该框架配置为渐变的非跨识别（GNC）算法，以连续解决两个非凸子问题，而无需初始猜测。得益于两个子问题的线性属性，我们的框架只需要线性求解器才能最佳地解决GNC中遇到的优化问题。我们广泛验证了所提出的框架，该框架称为平面PGO基准中的Degnc-Laf（具有线性角度公式的脱钩的非跨识别）。事实证明，它的运行速度明显比标准和通用GNC快30倍（有时超过30倍），同时导致高质量的估计值。
We propose a robust framework for the planar pose graph optimization contaminated by loop closure outliers. Our framework rejects outliers by first decoupling the robust PGO problem wrapped by a Truncated Least Squares kernel into two subproblems. Then, the framework introduces a linear angle representation to rewrite the first subproblem that is originally formulated with rotation matrices. The framework is configured with the Graduated Non-Convexity (GNC) algorithm to solve the two non-convex subproblems in succession without initial guesses. Thanks to the linearity properties of both the subproblems, our framework requires only linear solvers to optimally solve the optimization problems encountered in GNC. We extensively validate the proposed framework, named DEGNC-LAF (DEcoupled Graduated Non-Convexity with Linear Angle Formulation) in planar PGO benchmarks. It turns out that it runs significantly (sometimes up to over 30 times) faster than the standard and general-purpose GNC while resulting in high-quality estimates.
