Addressing data association by message passing over graph neural networks
Bernardo Camajori Tedeschini, Mattia Brambilla, Luca Barbieri, Monica Nicoli
Abstract
In cooperative localization systems, the fusion of information from multiple sensing platforms is acknowledged to improve localization accuracy of sensed targets. However, the data association required to perform the inference is non-trivial to be solved. In this context, we propose a graph formulation of the data association problem among unlabelled information produced at different sensors in which we run a Message Passing Neural Network (MPNN). The proposed MPNN algorithm suits for centralized sensing architectures where all sensors are connected to a single processing unit. We validate the theoretic aspects with numerical simulations in a vehicular scenario with cooperative lidar sensing. We show the robustness of the model against several environmental complexities such as high number of cooperative vehicles and different noise intensities.