Asynchronous Distributed Smoothing and Mapping via On-Manifold Consensus ADMM

TL;DR

This paper introduces MESA, a fully distributed, asynchronous optimization algorithm based on Consensus ADMM, designed for multi-robot CSLAM, demonstrating superior convergence and accuracy over existing methods.

Contribution

The paper presents MESA, a novel distributed, asynchronous CSLAM back-end algorithm that is general-purpose and improves convergence and accuracy compared to prior solutions.

Findings

MESA outperforms existing CSLAM back-ends in convergence speed.

MESA achieves higher accuracy in multi-robot localization.

MESA tolerates communication delays and robot failures effectively.

Abstract

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.