论文标题
姿势图优化的广义近端方法
Generalized Proximal Methods for Pose Graph Optimization
论文作者
论文摘要
在本文中，我们将最初设计的近端方法推广到特殊欧几里得组上的非convex姿势姿势优化（PGO），并在特殊的欧几里得组上进行了优化，并表明我们提出的普遍近端方法将PGO收敛到一阶关键点。此外，我们提出的方法几乎不会损失任何理论保证，从而显着加速了收敛速率。此外，我们所提出的方法可以轻松分布和并行，而无需妥协效率。这项工作的功效是通过对同时本地化和映射（SLAM）和分布式3D传感器网络定位的实施来验证的，这表明我们提出的方法比现有技术要快得多，以使其融合到足够的实际使用精度。
In this paper, we generalize proximal methods that were originally designed for convex optimization on normed vector space to non-convex pose graph optimization (PGO) on special Euclidean groups, and show that our proposed generalized proximal methods for PGO converge to first-order critical points. Furthermore, we propose methods that significantly accelerate the rates of convergence almost without loss of any theoretical guarantees. In addition, our proposed methods can be easily distributed and parallelized with no compromise of efficiency. The efficacy of this work is validated through implementation on simultaneous localization and mapping (SLAM) and distributed 3D sensor network localization, which indicate that our proposed methods are a lot faster than existing techniques to converge to sufficient accuracy for practical use.
