Abstract: The covariance intersection (CI) framework represents a generalization of the Kalman filter that permits filtering and estimation to be performed in the presence of unmodeled correlations. As described in previous papers, unmodeled correlations arise in virtually all real-world problems; but in many applications the correlations are so significant that they cannot be 'swept under the rug' simply by injecting extra stabilizing noise within a traditional Kalman filter. In this paper we briefly describe some of the properties of the CI algorithm and demonstrate their relevance to the notoriously difficult problem of simultaneous map building and localization for autonomous vehicles. !7
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