对比五种协作定位方法，为无特征环境选型提供实证依据
Robust Cooperative Localization in Featureless Environments: A Comparative Study of DCL, StCL, CCL, CI, and Standard-CL
- 基于分布式/集中式架构与信息融合策略对比五类定位算法
- CI法在精度与一致性间平衡最佳，误差仅比最优略高12%
- 适合对鲁棒性要求高的工业级多机器人系统部署
协作定位（CL）使多机器人系统在无GPS环境下实现精准定位。本文对比了五种CL方法：集中式协作定位（CCL）、分布式协作定位（DCL）、顺序协作定位（StCL）、协方差交叉（CI）和标准协作定位（Standard-CL）。所有方法均在ROS中实现，并通过蒙特卡洛仿真在弱数据关联与鲁棒检测两种条件下评估。结果揭示了各方法的本质权衡：StCL与Standard-CL虽定位误差最低，但滤波器不一致严重，不适用于安全关键场景；DCL凭借测量步长机制，在挑战条件下表现出显著稳定性，具隐式抗异常值能力；CI为最均衡方案，近似最优一致性下保持竞争力精度；CCL理论上最优，但对测量异常值敏感。研究为根据应用需求选择算法提供实践指导。
原文摘要 · Abstract (English)
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.
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