Vision-Based Localization in Multi-Agent Networks With Communication Constraints

Among the diversified applications of Internet of Vehicles (IoV), vision-based localization has raised increasing concern for its extraordinary performance in terms of accuracy and flexibility. The ultra-reliability and low-latency requirements of IoV have posed urgent demand for the provision of precise relative network geometry and efficient data transmission. In this paper, we propose a vision-based relative localization scheme in the presence of communication constraints. First, the relative squared position error bound (SPEB) is derived via subspace projection of the Fisher information matrix. Next, we determine the local convexity of the relative SPEB with respect to bit allocation vectors and propose two bit allocation algorithms. Furthermore, we exploit the vision-based relative geometry and develop two localization algorithms based on Euclidean distance matrix completion. The simulation results validate that the proposed vision-based localization algorithms achieve near-optimal performance in terms of the relative SPEB under bandwidth constraints, as well as demonstrate the superiority of adopting cooperation among mobile agents.