The development of multi-robot swarm technology represents a transformative frontier in robotics, offering vast potential across industries such as disaster response, environmental monitoring, logistics, and large-scale agricultural operations. In this scenario, the ability of robots to effectively localize themselves becomes paramount. However, traditional centralized methods quickly become impractical, suffering from communication bottlenecks, limited scalability, and vulnerability to system failures. This challenge underscores the need for distributed solutions, where each robot independently contributes to the overall system, ensuring robust and scalable performance. The aim of this thesis is to investigate the capability of factor graphs to model the complexities of multi-robot systems and examine how Gaussian Belief Propagation (GBP) enables efficient inference on such models.