One-dimensional Cooperative Localization for Vehicles equipped with mono-frequency GNSS receivers

Elwan Héry (1) , Philippe Xu (1) , Philippe Bonnifait (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

— Localization is an important issue for autonomous vehicle navigation. In this paper, we present a one-dimensional cooperative localization problem by using curvilinear abscissa with respect to a reference map. This is made possible by using a high-definition map containing accurate information, such as lane markings which can be used to reduce significantly the cross-track and heading errors. In the context of cooperative localization, a vehicle can use its perception capabilities along with V2V communication to retrieve the relative pose of another vehicle in order to improve the accuracy of its own pose estimate. Experimental results with non independent data coming from communication devices show the necessity to use a more consistent algorithm for data fusion than the classical Kalman filter, such as covariance intersection. The results presented in this paper also show that the one-dimensional cooperative localization algorithm is particularly useful on curved roads since the accuracy increases when cooperative vehicles drive in such conditions.

Domaines: Robotique [cs.RO]

Elwan Héry, Philippe Xu, Philippe Bonnifait. One-dimensional Cooperative Localization for Vehicles equipped with mono-frequency GNSS receivers. European Navigation Conference (ENC 2017), May 2017, Lausanne, Switzerland. ⟨hal-01596883v2⟩