论文标题
分布式姿势图优化的大量最小化方法具有收敛保证
Majorization Minimization Methods for Distributed Pose Graph Optimization with Convergence Guarantees
论文作者
论文摘要
在本文中，我们考虑了在多机器人同时定位和映射（SLAM）中具有广泛应用的分布式姿势图优化（PGO）的问题。我们提出了将大分化方法最小化的方法来分布PGO，并表明我们所提出的方法可以在轻度条件下收敛到一阶关键点。此外，由于我们提出的方法依赖于分布式PGO的近端运算符，因此可以使用Nesterov的方法显着加速收敛速率，更重要的是，加速度不会引起理论保证的妥协。此外，我们还向具有二次收敛的分布式和弦初始化提出了加速的大分化方法，该方法可用于计算分布式PGO的初始猜测。这项工作的功效是通过在2D和3D SLAM数据集上的应用程序中验证的，并与现有的最新方法进行了比较，这表明我们所提出的方法具有更快的收敛性，并为分布式PGO提供了更好的解决方案。
In this paper, we consider the problem of distributed pose graph optimization (PGO) that has extensive applications in multi-robot simultaneous localization and mapping (SLAM). We propose majorization minimization methods to distributed PGO and show that our proposed methods are guaranteed to converge to first-order critical points under mild conditions. Furthermore, since our proposed methods rely a proximal operator of distributed PGO, the convergence rate can be significantly accelerated with Nesterov's method, and more importantly, the acceleration induces no compromise of theoretical guarantees. In addition, we also present accelerated majorization minimization methods to the distributed chordal initialization that have a quadratic convergence, which can be used to compute an initial guess for distributed PGO. The efficacy of this work is validated through applications on a number of 2D and 3D SLAM datasets and comparisons with existing state-of-the-art methods, which indicates that our proposed methods have faster convergence and result in better solutions to distributed PGO.
