Vehicle-infrastructure localization based on the SME filter

Precise and accurate localization is important for safe autonomous driving. Given a traffic scenario with multiple vehicles equipped with proprioceptive sensors for self-localization and infrastructure equipped with exteroceptive sensors for car detection, vehicle-infrastructure communication can be used to improve the localization. However as the number of vehicles in a scenario increases, data association becomes increasingly challenging. We propose a solution utilizing the symmetric measurement equation filter (SME) for cooperative localization to address data association issues, as it does not require an enumeration of measurement-to-target associations. The key idea is to define a symmetric transformation which maps position measurements to a homogeneous function, thereby effectively addressing several challenges in vehicle-infrastructure scenarios such as bandwidth limitations, data association challenges and especially the configuration of the exteroceptive sensor. The approach works well even in the case that the location and orientation of the exteroceptive sensor are unknown. To the best of our knowledge, our proposed solution is among the first to address all these challenges of cooperative localization simultaneously, by utilizing the topology information of the vehicles. A comparative study based on simulations demonstrates the reliability and the feasibility of the proposed approach in 2D coordinates.