Multiple vehicle cooperative localization under random finite set framework

Abstract

Abstract-This paper presents a new multiple vehicle cooperative localization approach based on Random Finite Set (RFS) theory. Assuming vehicles are equipped with proprioceptive and exteroceptive sensors to localize the positions, a solution based on RFS statistics is therefore proposed to consider the whole group behavior instead of each vehicle. For this, we rely on Probability Hypothesis Density (PHD) filtering. Compared to other methods, our approach presents a recursive filtering algorithm that provides dynamic estimation of multiple vehicle states. The proposed method addresses the current challenges in multiple vehicle cooperative localization domain such as communication bandwidth issue, data association uncertainty and the over-convergence problem.A comparative study based on simulations demonstrates the reliability and the feasibility of the proposed approach in large scale environments.

Vehicle-Infrastructure Localization Based on the SME Filter

“…However, the challenge is the over-convergence problem. This is due to the stochastic interdependence between the estimations when sharing the information [3]. Another problem may also influence the localization: assuming the exteroceptive sensor is moving with high speed, the uncertainty of its estimation could become big and influence the transformation function calculation.…”

“…Since GPS is susceptible to interference or even not fully available, for example in tunnels or underground, the localization task can be supported by utilizing both proprioceptive and exteroceptive sensors [2], [3]. Furthermore, with the development of Car-2-Car (C2C) and Car-2-Infrastructure (C2I) communication techniques, sharing information, like observations and state estimations, across the whole network becomes possible [4].…”