Nondivergent simultaneous map building and localization using covariance intersection.
In: Speigle, SA, (ed.)
NAVIGATION AND CONTROL TECHNOLOGIES FOR UNMANNED SYSTEMS II.
(pp. 2 - 11).
SPIE - INT SOC OPTICAL ENGINEERING
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.
|Title:||Nondivergent simultaneous map building and localization using covariance intersection|
|Event:||Conference on Navigation and Control Technologies for Unmanned Systems II|
|Keywords:||autonomous vehicles, data fusion, filtering, Covariance Intersection, Kalman filter, map building, matrix inequalities, nonlinear filtering|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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