Robust Cooperative Localization in Featureless Environments: A Comparative Study of DCL, StCL, CCL, CI, and Standard-CL

2026-03-10

AI Summary

This study addresses the challenges of robustness and consistency in multi-robot cooperative localization under featureless, GPS-denied environments. The authors systematically evaluate five cooperative localization methods—CCL, DCL, StCL, CI, and Standard-CL—under conditions of weak data association and strong detection, implementing all approaches within ROS and comparing their performance via Monte Carlo simulations. The analysis reveals a fundamental trade-off between estimation accuracy and filter consistency: StCL and Standard-CL achieve the highest accuracy but exhibit poor consistency; CCL is theoretically optimal yet highly sensitive to outliers; DCL demonstrates robust performance in challenging scenarios, with its implicit regularization mechanism elucidated; and CI strikes a favorable balance between accuracy and consistency. This work provides both theoretical insights and practical guidance for selecting cooperative localization methods in real-world deployments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Cooperative localization (CL) enables accurate position estimation in multi-robot systems operating in GPS-denied environments. This paper presents a comparative study of five CL approaches: Centralized Cooperative Localization (CCL), Decentralized Cooperative Localization (DCL), Sequential Cooperative Localization (StCL), Covariance Intersection (CI), and Standard Cooperative Localization (Standard-CL). All methods are implemented in ROS and evaluated through Monte Carlo simulations under two conditions: weak data association and robust detection. Our analysis reveals fundamental trade-offs among the methods. StCL and Standard-CL achieve the lowest position errors but exhibit severe filter inconsistency, making them unsuitable for safety-critical applications. DCL demonstrates remarkable stability under challenging conditions due to its measurement stride mechanism, which provides implicit regularization against outliers. CI emerges as the most balanced approach, achieving near-optimal consistency while maintaining competitive accuracy. CCL provides theoretically optimal estimation but shows sensitivity to measurement outliers. These findings offer practical guidance for selecting CL algorithms based on application requirements.

Problem

• Cooperative Localization
• Featureless Environments
• GPS-denied
• Filter Consistency
• Robustness

Innovation

• Cooperative Localization
• Measurement Stride Mechanism
• Covariance Intersection
• Filter Consistency
• Robustness in Featureless Environments