Abstract
This paper studies the problem of multiple vehicle cooperative localization with spatial registration in the formulation of the probability hypothesis density (PHD) filter. Assuming vehicles are equipped with proprioceptive and exteroceptive sensors (with biases) to cooperatively localize positions, a simultaneous solution for joint spatial registration and state estimation is proposed. For this, we rely on the sequential Monte Carlo implementation of the PHD filtering. Compared to other methods, the concept of multiple vehicle cooperative localization with spatial registration is first proposed under Random Finite Set Theory. In addition, the proposed solution also addresses the challenges for multiple vehicle cooperative localization, e.g., the communication bandwidth issue and data association uncertainty. The simulation result demonstrates its reliability and feasibility in large-scale environments.
Highlights
Accurate vehicle localization is the current trend in the field of intelligent vehicles for the purpose of autonomous driving
In the probability hypothesis density (PHD) filter, the collection of individual targets is treated as a set-valued state, and the collection of individual observations is treated as a set-valued observation
This paper extends the earlier work for multiple vehicle cooperative localization [18] by utilizing the sequential Monte Carlo PHD solution
Summary
Accurate vehicle localization is the current trend in the field of intelligent vehicles for the purpose of autonomous driving. To the best of the authors’ knowledge, the spatial misregistration problem has not been considered in multiple vehicle cooperative localization under Random Finite Set Theory. The multiple vehicle states and the biases of the sensors are jointly estimated recursively via the PHD filter. The contributions of the proposed approach are as follows: We are among the first to consider multiple vehicle cooperative localization with unknown biases under Random Finite Set Theory. By utilizing the PHD filter, the challenges for multiple vehicle cooperative localization are overcome [18], e.g., the communication bandwidth is bounded and the data association issue is eliminated. The rest of this paper is organized as follows: Section 2 briefly describes multiple vehicle cooperative localization with measurement misregistration.
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