Graph Laplacian Extended Kalman Filter for Connected and Automated Vehicles Localization

Extended Kalman Filters have been widely applied for tracking the location of moving semi-autonomous vehicles.
The latter are equipped with a multitude of sensors generating multi-modal data, while at the same time they are capable of
cooperating via Vehicle-to-Vehicle communication technologies. In this paper, we have formulated a cooperative tracking scheme
based on Extended Kalman Filter, in order to cope with erroneous  GPS location information. It performs multi-modal fusion in a
centralized and distributed manner, assuming the existence of an overall fusion center or local interaction among neighbouring
and connected vehicles only. It features the property of encoding in a linear form the different measurement modalities, including
range and GPS measurements, exploiting the connectivity topology of cooperating vehicles, using the graph Laplacian
operator.  The extended experimental evaluation using realistic  vehicle trajectories extracted by CARLA autonomous driving
simulator, verify the significant reduction of GPS error under various realistic conditions. Moreover, both schemes outperform
existing cooperative localization methods. Finally, the distributed tracking approach exhibits similar performance and in specific
cases outperforms the centralized counterpart.