In this paper we present a fully distributed, asynchronous, and general purpose optimization algorithm for Consensus Simultaneous Localization and Mapping (CSLAM). Multi-robot teams require that agents have timely and accurate solutions to their state as well as the states of the other robots in the team. To optimize this solution we develop a CSLAM back-end based on Consensus ADMM called MESA (Manifold, Edge-based, Separable ADMM). MESA is fully distributed to tolerate failures of individual robots, asynchronous to tolerate communication delays and outages, and general purpose to handle any CSLAM problem formulation. We demonstrate that MESA exhibits superior convergence rates and accuracy compare to existing state-of-the art CSLAM back-end optimizers.

翻译：本文提出了一种完全分布式、异步且通用的协同同时定位与建图（CSLAM）优化算法。多机器人团队要求各智能体能够及时获取自身及其他机器人状态的精确解。为优化该解，我们开发了基于一致性ADMM的CSLAM后端算法——MESA（流形、边基、可分离ADMM）。MESA具有完全分布式特性以应对单机器人故障，异步特性以应对通信延迟与中断，且具备通用性以处理任意CSLAM问题表述。实验表明，与现有最优CSLAM后端优化器相比，MESA展现出更优的收敛速度与精度。