Robust and Convergent Distributed Cooperative Localization with Labelled Bernoulli Random Finite Set

Cooperative localization (CL) is critical for multirobot systems to accurately ascertain their positions in the environment. This article presents a robust and convergent distributed CL algorithm to effectively address localization inaccuracies and inconsistency caused by intermittent or limited absolute observation capabilities. The algorithm integrates three key modules: propagation, observation, and communication, enabling each robot to estimate its states and measure noise covariance simultaneously. To enhance the estimation consistency, intervehicle relative observations and landmark absolute observations are modeled as multi-Bernoulli random finite sets (RFSs), with robot states updated using a coupled correlation scheme. By combining extended particle filtering and covariance intersection (CI) techniques, the algorithm efficiently handles intermittent observations, leading to substantial improvements in localization accuracy and estimation consistency. Furthermore, the proofs of convergent consistency are provided in this article, validating the algorithm's robustness and convergence.