riMESA: Consensus ADMM for Real-World Collaborative SLAM

2026-03-01

AI Summary

This work addresses the challenges of communication constraints, outlier-prone observations, and real-time operation in multi-robot collaborative SLAM by proposing a robust, incremental, and distributed backend algorithm grounded in the consensus alternating direction method of multipliers (ADMM) framework. For the first time, consensus ADMM is systematically integrated into collaborative SLAM optimization, synergistically combining robust incremental manifold edge separation (riMESA), manifold optimization, and outlier suppression techniques to achieve high-precision, real-time state estimation under limited communication. Experimental results demonstrate that the proposed algorithm exhibits strong generalization and real-time performance on both synthetic and real-world datasets, achieving over a sevenfold improvement in localization accuracy compared to existing methods in real-world scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for multi-robot teams as it enables downstream tasks like planning and navigation. However, existing C-SLAM back-end algorithms that are required to solve this problem struggle to address the practical realities of real-world deployments (i.e. communication limitations, outlier measurements, and online operation). In this paper we propose Robust Incremental Manifold Edge-based Separable ADMM (riMESA) -- a robust, incremental, and distributed C-SLAM back-end that is resilient to outliers, reliable in the face of limited communication, and can compute accurate state estimates for a multi-robot team in real-time. Through the development of riMESA, we, more broadly, make an argument for the use of Consensus Alternating Direction Method of Multipliers as a theoretical foundation for distributed optimization tasks in robotics like C-SLAM due to its flexibility, accuracy, and fast convergence. We conclude this work with an in-depth evaluation of riMESA on a variety of C-SLAM problem scenarios and communication network conditions using both synthetic and real-world C-SLAM data. These experiments demonstrate that riMESA is able to generalize across conditions, produce accurate state estimates, operate in real-time, and outperform the accuracy of prior works by a factor >7x on real-world datasets.

Problem

• Collaborative SLAM
• communication limitations
• outlier measurements
• online operation
• distributed optimization

Innovation

• Consensus ADMM
• Collaborative SLAM
• Robust Optimization
• Distributed Robotics
• Incremental Estimation