A Study of Different Observation Models for Cooperative Localization in Platoons

Elwan Héry (1, 2, 3) , Philippe Xu (4) , Philippe Bonnifait (4)

1 Nantes Univ - ECN - NANTES UNIVERSITÉ - École Centrale de Nantes
2 LS2N - équipe ARMEN - Autonomie des Robots et Maîtrise des interactions avec l’ENvironnement
3 LS2N - Laboratoire des Sciences du Numérique de Nantes
4 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

Localization and perception for autonomous vehicles are often studied separately. However, they can be regroup on a dynamic map representing the environment of the vehicle. This dynamic map can be exchanged with other vehicles to be fused with their own dynamic maps to increase their accuracy. This paper presents a decentralized data fusion method for cooperative localization based on both Extended Kalman Filter and Covariance Intersection Filter. Different observation models of the relative measurements from the perception (Cartesian and polar relative poses, distances, bearings and relative yaws) are compared. The approach is tested on data for 10 vehicles generated from a real dataset and completed with a simulated perception.

Mots clés: Location awareness; Data integration; Data models; Kalman filters; Vehicle dynamics; Autonomous vehicles; Intelligent transportation systems

Domaines: Automatique / Robotique

Elwan Héry, Philippe Xu, Philippe Bonnifait. A Study of Different Observation Models for Cooperative Localization in Platoons. 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Philippe Xu; Philippe Bonnifait; Claus Brenner; Hao Cheng; Javier Ibanez-Guzman; Steffen Schön; Jingyao Su; Jeldrik Axmann, Sep 2023, Bilbao, Spain. ⟨10.1109/ITSC57777.2023.10422253⟩. ⟨hal-04344334⟩