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

Nivand Khosravi; Rodrigo Ventura; Meysam Basiri

cs.ROMar 10, 2026

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

Authors: Nivand Khosravi, Rodrigo Ventura, Meysam Basiri

Organizations: Department of Electrical and Computer Engineering, Institute for Systems and Robotics (ISR), Instituto Superior T´ecnico, Lisbon, Portugal

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.

Figures & tables

Method	Update	$\mathbf{P}_{12}$	Consistent	Complexity
CCL	Joint	Tracked	Yes	$O(N^{3})$
DCL	Joint + stride	Tracked	Yes	$O(N^{3}/s)$
StCL	Sequential	Discarded	No	$O(N^{2})$
CI	Fusion	Conservative	Yes	$O(N^{2})$ + opt.
Standard-CL	Joint	Discarded	No	$O(N^{2})$

TABLE I: Summary of Cooperative Localization Methods

Fig. 1: State estimation trajectories for a representative run. Top row: Robot 1. Bottom row: Robot 2. DCL (green) shows visible drift due to measurement stride, while other methods track ground truth closely.

Condition	Algorithm	RMSE (m)	Std (m)	Robot 1	Robot 2	Time (ms)	NEES	NIS
Robust Detection	StCL	0.140	0.045	0.133	0.147	1.58	13.97	3.31
	Standard-CL	0.148	0.062	0.133	0.163	2.28	15.39	3.30
	CI	0.183	0.065	0.179	0.187	13.94	1.99	0.88
	CCL	0.220	0.044	0.163	0.277	3.05	0.70	3.59
	DCL	0.223	0.099	0.181	0.266	0.70	0.69	3.47
Weak Association	StCL	0.160	0.075	0.157	0.163	1.51	36.10	2.53

TABLE II: Performance Comparison Under Robust Detection and Weak Data Association (5 Runs Each)

Fig. 2: RMSE (boxplots, left) and NEES (squares, right) under (a) weak association and (b) robust detection.

Cooperative Localization

Cooperative localization lets robots and other agents estimate their positions by combining onboard motion sensing with shared relative measurements.

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Mar 12, 2026J/K move · Enter open · S save

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

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

Department of Electrical and Computer Engineering, Instituto Superior T´ecnico, Lisbon, Portugal · Department of Mechanical Engineering, Yazd University, Yazd, Iran · Department of Mechanical Engineering, University of Birmingham, Birmingham, United Kingdom

Cooperative Localization

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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.

Abstract

Cooperative Localization

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