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

Nivand Khosravi, Niusha Khosravi, Mohammad Bozorg, Masoud S. Bahraini

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.

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

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.
Paper Structure (17 sections, 10 equations, 6 figures, 1 table)

This paper contains 17 sections, 10 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contributions
  4. Proposed Decentralized Cooperative Localization
  5. Problem Formulation and System Architecture
  6. DCL Framework
  7. Prediction Stage
  8. Measurement and Update
  9. Feature Extraction and Landmark Detection
  10. Asynchronous Data Synchronization Implementation
  11. Dual-Landmark Integration Strategy
  12. Simulation Validation
  13. Experimental Validation
  14. Real-World Experimental Setup
  15. Experimental Results
  16. ...and 2 more sections

Figures (6)

  • Figure 1: Relative measurement between Robot 1 (with LiDAR) and Robot 2 (dynamic landmark).
  • Figure 2: DCL framework with distributed processing, asynchronous sensor fusion via ROS message_filters, and real-time feature extraction. Data exchange occurs only during mutual observations.
  • Figure 3: LiDAR segmentation and cylindrical landmark extraction results. Left: ABD-based segmentation showing detected environmental and landmark features in polar and Cartesian views. Right: cylindrical feature extraction isolating the dynamic landmark as a single detected point.
  • Figure 4: Experimental workspace showing two differential-drive robots. Robot 2 (right) carries a cylindrical landmark.
  • Figure 5: RViz visualization displaying RPLiDAR scan data during experimental trials.
  • ...and 1 more figures