论文标题
异步和并行分布的姿势图优化
Asynchronous and Parallel Distributed Pose Graph Optimization
论文作者
论文摘要
我们提出异步随机并行姿势图优化（ASAPP），这是在多机器人同时定位和映射中用于分布式姿势图优化（PGO）的第一种异步算法。通过使机器人在不同步的情况下优化其本地轨迹估计值，ASAPP提供了抵御通信延迟的弹性，并减轻了等待网络中的散落者的需求。此外，ASAPP可以应用于PGO的等级限制性放松，这是一类关键的非凸轮Riemannian优化问题，这是最近在全球最佳PGO上进行突破的基础。在有限的延迟下，我们使用足够小的步骤建立了ASAPP的全局一阶收敛。派生的步骤尺寸取决于最坏情况下的延迟和固有的问题稀疏性，此外，当没有延迟时，同步算法的已知结果匹配。与最新同步方法相比，对模拟和现实世界数据集的数值评估表明了有利的性能，并且在实践中显示了ASAPP对各种延误的弹性。
We present Asynchronous Stochastic Parallel Pose Graph Optimization (ASAPP), the first asynchronous algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous localization and mapping. By enabling robots to optimize their local trajectory estimates without synchronization, ASAPP offers resiliency against communication delays and alleviates the need to wait for stragglers in the network. Furthermore, ASAPP can be applied on the rank-restricted relaxations of PGO, a crucial class of non-convex Riemannian optimization problems that underlies recent breakthroughs on globally optimal PGO. Under bounded delay, we establish the global first-order convergence of ASAPP using a sufficiently small stepsize. The derived stepsize depends on the worst-case delay and inherent problem sparsity, and furthermore matches known result for synchronous algorithms when there is no delay. Numerical evaluations on simulated and real-world datasets demonstrate favorable performance compared to state-of-the-art synchronous approach, and show ASAPP's resilience against a wide range of delays in practice.
