Abstract
Localization is a key problem for autonomous vehicle navigation. The use of high-definition maps and perception algorithms allows now to have lane-level accurate pose estimation in terms of cross-track and heading error. In this paper, we focus on the along-track localization of cooperative vehicles. We introduce a one-dimensional formulation of the localization problem by considering curvilinear coordinates. The covariance intersection filter is derived in one dimension leading to a minimum variable filter which allows multiple vehicles to cooperate while keeping consistent localization estimates. We show that the along-track localization error is directly dependent on the relative orientation between the trajectories followed by the cooperating vehicles. Experiments with two autonomous electric vehicles were conducted to evaluate the proposed approach.
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