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 practical network conditions, 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、异步且通用的优化算法，用于一致性同时定位与建图（CSLAM）。多机器人团队要求各智能体能够及时且准确地获取自身及其他机器人的状态信息。为优化这一求解过程，我们开发了基于一致性ADMM的CSLAM后端算法——MESA（流形、边缘分离式ADMM）。MESA具备完全分布式特性以容忍单个机器人故障，支持异步处理以适应实际网络条件，同时具有通用性以处理任意CSLAM问题形式。实验证明，与现有最先进的CSLAM后端优化器相比，MESA展现出更优的收敛速率和精度。