Robust Cooperative Localization in Featureless Environments: A Comparative Study of DCL, StCL, CCL, CI, and Standard-CL
It provides practical guidance for selecting localization algorithms in safety-critical applications like robotics, though it is an incremental study comparing existing methods.
This paper compared five cooperative localization methods for multi-robot systems in GPS-denied environments, finding that Sequential and Standard-CL had the lowest position errors but severe inconsistency, while Covariance Intersection offered near-optimal consistency with competitive accuracy.
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.