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

Cooperative localization is critical for multi-robot systems to accurately ascertain their positions in the environment. This paper presents a robust and convergent distributed cooperative localization 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, inter-vehicle 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 techniques, the algorithm efficiently handles intermittent observations, leading to substantial improvements in localization accuracy and estimation consistency. Further, the proofs of convergent consistency are provided in the paper, validating the algorithm's robustness and convergence.