Nondivergent simultaneous map-building and localization using covariance intersection

Jeffrey K. Uhlmann; Simon J. Julier; Michael Csorba

doi:10.1117/12.277216

26 June 1997 Nondivergent simultaneous map building and localization using covariance intersection

Jeffrey K. Uhlmann, Simon J. Julier, Michael Csorba

Proceedings Volume 3087, Navigation and Control Technologies for Unmanned Systems II; (1997) https://doi.org/10.1117/12.277216
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Jeffrey K. Uhlmann, Simon J. Julier, and Michael Csorba "Nondivergent simultaneous map building and localization using covariance intersection", Proc. SPIE 3087, Navigation and Control Technologies for Unmanned Systems II, (26 June 1997); https://doi.org/10.1117/12.277216

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