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.

翻译：协同定位（CL）能够在全球定位系统（GPS）拒止环境中实现多机器人系统的精确位置估计。本文对五种协同定位方法进行了比较研究：集中式协同定位（CCL）、分布式协同定位（DCL）、顺序协同定位（StCL）、协方差交集（CI）以及标准协同定位（Standard-CL）。所有方法均在ROS中实现，并通过蒙特卡洛仿真在两种条件下进行评估：弱数据关联与鲁棒检测。我们的分析揭示了这些方法之间的基本权衡。StCL和Standard-CL实现了最低的位置误差，但表现出严重的滤波器不一致性，使其不适用于安全关键型应用。DCL凭借其测量步幅机制，在挑战性条件下表现出显著的稳定性，该机制提供了对异常值的隐式正则化。CI成为最均衡的方法，在保持竞争力精度的同时实现了近乎最优的一致性。CCL提供了理论上的最优估计，但对测量异常值表现出敏感性。这些发现为根据应用需求选择CL算法提供了实用指导。