Multi-Vehicle Cooperative SLAM Using Iterated Split Covariance Intersection Filter

Simultaneous localization and mapping (SLAM) is important to outdoor intelligent vehicle applications. Multivehicle cooperative SLAM which takes advantage of data sharing can outperform single vehicle SLAM. This paper proposes a multi-vehicle cooperative SLAM method using iterated split covariance intersection filter (Iterated Split CIF). In the proposed method, a vehicle can flexibly perform cooperative SLAM with other vehicles in decentralized way, without complicated monitoring and controlling of data flow. Moreover, the innovation and observation outliers caused by modeling errors or abnormal measurements can be solved reliably. A simulation-based comparative study demonstrates the potential and advantage of the proposed multi-vehicle cooperative SLAM using Iterated Split CIF in terms of accuracy and robustness.