Cooperative vehicle-infrastructure localization based on the symmetric measurement equation filt

Precise and accurate localization is important for safe autonomous driving. Given a traffic scenario which has multiple vehicles equipped with internal sensors for self-localization, and external sensors from the infrastructure for vehicle localization, vehicle-infrastructure communication can be used to improve the accuracy and precision of localization. However, as the number of vehicles in a scenario increases, associating measurement data with the correct source becomes increasingly challenging. We propose a solution utilizing the symmetric measurement equation filter (SME) for cooperative localization to address data association issue, as it does not require an enumeration of measurement-to-target associations. The principal idea is to define a symmetrical transformation which maps measurements to a homogeneous function, thereby effectively addressing several challenges in vehicle-infrastructure scenarios such as data association, bandwidth limitations and registration/configuration of the external sensor. To the best of our knowledge, the proposed solution is among the first to address all these issues of cooperative localization simultaneously, by utilizing the topology information of the vehicles.