D-CLIPSE: Distributed Consensus-based Localization with Passive Listening on Shared State Exchange

2026-07-08

AI Summary

Achieving high-precision and consistent localization in multi-robot systems under constraints of limited hardware, communication, and computational resources remains challenging. This work proposes a consensus-based distributed filtering framework that integrates preintegrated odometry, shared state estimates, and a passive listening mechanism to simultaneously enable self-localization and neighbor state estimation with low communication overhead. The approach significantly enhances both localization accuracy and consistency, closely approximating the performance of centralized solutions while outperforming existing decentralized methods in both simulation and real-world experiments.

Abstract

Multi-robot localization that is accurate and consistent is imperative for downstream tasks such as planning and control. Centralized filtering approaches optimally fuse all available sensor measurements of the team. However, a centralized solution is rarely implementable due to hardware, communication, and computational constraints. Distributed approaches deploy a filter on each robot to estimate their own state and neighbours' states using inter-robot communication. This paper proposes a consistent, communication-efficient, and consensus-based distributed filtering framework that shares both preintegrated odometry and relevant shared states among communicating robots. The proposed method is validated in simulated and experimental scenarios, showing near centralized performance in accuracy, and especially in consistency, compared to the current state-of-the-art decentralized approach.

Problem

• multi-robot localization
• distributed filtering
• state estimation
• consistency
• communication constraints

Innovation

• distributed localization
• consensus-based filtering
• multi-robot systems
• communication efficiency
• state consistency