This work builds upon the Gaussian Belief Propagation (GBP) stack, utilizing it as the core framework for distributed multi-robot exploration missions. The GBP stack’s key strength lies in its single-factor graph representation of competencies such as planning, map consensus, and task allocation, making it a powerful tool for intelligent and collaborative robotic behavior. However, previous applications of GBP stack were limited to open environments without obstacles. To address this limitation, we extend the GBP stack by representing the environment as a navigational graph, allowing for task allocation and global path planning in more complex, obstacle-filled environments. This enhancement integrates seamlessly with the single-factor graph approach, enabling both navigational tasks and symbolic tasks, such as opening doors or performing inspections, to be efficiently distributed among a heterogeneous fleet of robots. A feature absent in earlier versions. Simulation results demonstrate improved task coordination, exploration efficiency, and adaptability to varied tasks, showcasing the potential of this extended framework for more challenging multirobot exploration scenarios.