Decentralized Cooperative Localization for Multi-Robot Systems with Asynchronous Sensor Fusion

TL;DR

This paper introduces a decentralized cooperative localization framework for multi-robot systems that operates effectively in GPS-denied environments, handling asynchronous sensor data, arbitrary initial orientations, and feature-sparse conditions, with proven superior accuracy over centralized methods.

Contribution

The paper presents a novel decentralized localization approach that manages asynchronous data, arbitrary initial orientations, and integrates static and dynamic landmarks, outperforming centralized methods in accuracy.

Findings

Achieves 34% RMSE reduction compared to centralized methods.

Dual-landmark framework improves localization accuracy by 56%.

Effective in feature-sparse and enclosed environments.

Abstract

Decentralized cooperative localization (DCL) is a promising approach for nonholonomic mobile robots operating in GPS-denied environments with limited communication infrastructure. This paper presents a DCL framework in which each robot performs localization locally using an Extended Kalman Filter, while sharing measurement information during update stages only when communication links are available and companion robots are successfully detected by LiDAR. The framework preserves cross-correlation consistency among robot state estimates while handling asynchronous sensor data with heterogeneous sampling rates and accommodating accelerations during dynamic maneuvers. Unlike methods that require pre-aligned coordinate systems, the proposed approach allows robots to initialize with arbitrary reference-frame orientations and achieves automatic alignment through transformation matrices in both the prediction and update stages. To improve robustness in feature-sparse environments, we introduce a dual-landmark evaluation framework that exploits both static environmental features and mobile robots as dynamic landmarks. The proposed framework enables reliable detection and feature extraction during sharp turns, while prediction accuracy is improved through information sharing from mutual observations. Experimental results in both Gazebo simulation and real-world basement environments show that DCL outperforms centralized cooperative localization (CCL), achieving a 34% reduction in RMSE, while the dual-landmark variant yields an improvement of 56%. These results demonstrate the applicability of DCL to challenging domains such as enclosed spaces, underwater environments, and feature-sparse terrains where conventional localization methods are ineffective.