Multi-Vehicle Cooperative Local Mapping Using Split Covariance Intersection Filter

Abstract

Local mapping plays an important role in outdoor intelligent vehicle applications and multi-vehicle cooperative local mapping which takes advantage of vehicular communication can bring considerable benefits to this important task. In this paper, a multi-vehicle cooperative local mapping architecture using split covariance intersection filter (Split CIF) is proposed. In the proposed method, a vehicle can flexibly perform cooperative local mapping with other vehicles in decentralized way, without complicated monitoring and controlling of data flow among vehicles; fused maps can be shared freely among vehicles. An efficient and accurate implementation of the Split CIF is also introduced. A simulation-based comparative study demonstrates the potential and advantage of the proposed multi-vehicle cooperative local mapping architecture using Split CIF.