Asynchronous Distributed Smoothing and Mapping via On-Manifold Consensus ADMM
arxiv(2023)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要