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.

翻译：分布式协同定位（DCL）是一种适用于在GPS拒止且通信基础设施有限的环境中运行的非完整移动机器人的有前景的方法。本文提出了一种DCL框架，其中每个机器人使用扩展卡尔曼滤波器在本地执行定位，仅在通信链路可用且LiDAR成功检测到同伴机器人时，在更新阶段共享测量信息。该框架保持了机器人状态估计之间的互相关一致性，同时处理具有异构采样率的异步传感器数据，并适应动态机动期间的加速度。与需要预对齐坐标系的方法不同，所提出的方法允许机器人以任意参考系方向初始化，并通过预测和更新阶段中的变换矩阵实现自动对齐。为了提高特征稀疏环境中的鲁棒性，我们引入了一种双路标评估框架，该框架同时利用静态环境特征和移动机器人作为动态路标。所提出的框架能够在急转弯期间实现可靠的检测和特征提取，同时通过相互观测的信息共享提高了预测精度。在Gazebo仿真和真实世界地下环境中的实验结果表明，DCL优于集中式协同定位（CCL），实现了34%的均方根误差降低，而双路标变体则带来了56%的改进。这些结果证明了DCL在封闭空间、水下环境和特征稀疏地形等传统定位方法无效的挑战性领域中的适用性。